def buildOccupationalTable(self, exposures):
colWidths = [1.7, 1, 1.7, 1.3, 2.0, 1.3]
tbl = TableMaker(colWidths, numHeaders=2, tblStyle="ntpTbl")
# write title
txt = "Occupational exposure data"
tbl.new_th(0, 0, txt, colspan=6)
# write header
tbl.new_th(1, 0, "Industry, country, year")
tbl.new_th(1, 1, "Job/process")
tbl.new_th(1, 2, "Mean, agent")
tbl.new_th(1, 3, "Range")
tbl.new_th(1, 4, "Comments/\nadditional data")
tbl.new_th(1, 5, "References")
row = 2
for exp in exposures:
for res in exp["results"]:
runs = [
tbl.new_run(exp["occupation"], newline=True, b=True),
tbl.new_run(
f'{exp["location"] or "Not-reported"}, {exp["collectionDate"]}',
newline=False,
),
]
tbl.new_td_run(row, 0, runs)
txt = exp["occupationInfo"] or ""
tbl.new_td_txt(row, 1, txt)
txt = (
f'{res["exposureLevel"]} {res["units"]} ({res["exposureLevelDescription"]}),'
f' {res["agent"]}'
)
tbl.new_td_txt(row, 2, txt)
txt = res["exposureRangePrint"]
tbl.new_td_txt(row, 3, txt)
txt = exp["comments"] or ""
tbl.new_td_txt(row, 4, txt)
txt = exp["reference"]["name"]
tbl.new_td_txt(row, 5, txt)
row += 1
tbl.render(self.doc)
self.doc.add_page_break()
def buildOccupationalTable(self, exposures):
colWidths = [1.7, 1, 1.7, 1.3, 2.0, 1.3]
tbl = TableMaker(colWidths, numHeaders=2, tblStyle="ntpTbl")
# write title
txt = "Occupational exposure data"
tbl.new_th(0, 0, txt, colspan=6)
# write header
tbl.new_th(1, 0, "Industry, country, year")
tbl.new_th(1, 1, "Job/process")
tbl.new_th(1, 2, "Mean, agent")
tbl.new_th(1, 3, "Range")
tbl.new_th(1, 4, "Comments/\nadditional data")
tbl.new_th(1, 5, "References")
row = 2
for exp in exposures:
for res in exp["results"]:
runs = [
tbl.new_run(exp["occupation"], newline=True, b=True),
tbl.new_run(
f'{exp["location"] or "Not-reported"}, {exp["collectionDate"]}',
newline=False,
),
]
tbl.new_td_run(row, 0, runs)
txt = exp["occupationInfo"] or ""
tbl.new_td_txt(row, 1, txt)
txt = (
f'{res["exposureLevel"]} {res["units"]} ({res["exposureLevelDescription"]}),'
f' {res["agent"]}')
tbl.new_td_txt(row, 2, txt)
txt = res["exposureRangePrint"]
tbl.new_td_txt(row, 3, txt)
txt = exp["comments"] or ""
tbl.new_td_txt(row, 4, txt)
txt = exp["reference"]["name"]
tbl.new_td_txt(row, 5, txt)
row += 1
tbl.render(self.doc)
self.doc.add_page_break()
def buildEnviroMixedTables(self, exposures, title):
colWidths = [2, 1.5, 1.5, 2, 2]
tbl = TableMaker(colWidths, numHeaders=2, tblStyle="ntpTbl")
# write title
tbl.new_th(0, 0, title, colspan=5)
# write header
tbl.new_th(1, 0, "Region, country (city)")
tbl.new_th(1, 1, "Mean, agent")
tbl.new_th(1, 2, "Range")
tbl.new_th(1, 3, "Comments/\nadditional data")
tbl.new_th(1, 4, "References")
row = 2
for exp in exposures:
for res in exp["results"]:
runs = [
tbl.new_run(exp["country"], newline=True, b=True),
tbl.new_run(
f'{exp["location"] or "Not-reported"}, {exp["collectionDate"]}',
newline=False,
),
]
tbl.new_td_run(row, 0, runs)
txt = (
f'{res["exposureLevel"]} {res["units"]} ({res["exposureLevelDescription"]}),'
f' {res["agent"]}'
)
tbl.new_td_txt(row, 1, txt)
txt = res["exposureRangePrint"]
tbl.new_td_txt(row, 2, txt)
txt = exp["comments"] or ""
tbl.new_td_txt(row, 3, txt)
txt = exp["reference"]["name"]
tbl.new_td_txt(row, 4, txt)
row += 1
tbl.render(self.doc)
self.doc.add_page_break()
def buildEnviroMixedTables(self, exposures, title):
colWidths = [2, 1.5, 1.5, 2, 2]
tbl = TableMaker(colWidths, numHeaders=2, tblStyle="ntpTbl")
# write title
tbl.new_th(0, 0, title, colspan=5)
# write header
tbl.new_th(1, 0, "Region, country (city)")
tbl.new_th(1, 1, "Mean, agent")
tbl.new_th(1, 2, "Range")
tbl.new_th(1, 3, "Comments/\nadditional data")
tbl.new_th(1, 4, "References")
row = 2
for exp in exposures:
for res in exp["results"]:
runs = [
tbl.new_run(exp["country"], newline=True, b=True),
tbl.new_run(
f'{exp["location"] or "Not-reported"}, {exp["collectionDate"]}',
newline=False,
),
]
tbl.new_td_run(row, 0, runs)
txt = (
f'{res["exposureLevel"]} {res["units"]} ({res["exposureLevelDescription"]}),'
f' {res["agent"]}')
tbl.new_td_txt(row, 1, txt)
txt = res["exposureRangePrint"]
tbl.new_td_txt(row, 2, txt)
txt = exp["comments"] or ""
tbl.new_td_txt(row, 3, txt)
txt = exp["reference"]["name"]
tbl.new_td_txt(row, 4, txt)
row += 1
tbl.render(self.doc)
self.doc.add_page_break()
def build_ROB_table(self, study):
widths = [2.8, 0.7, 4.0]
tbl = TableMaker(widths, numHeaders=2, firstRowCaption=True, tblStyle="ntpTbl")
# add caption
tbl.new_td_run(0, 0, self.getTableCaptionRuns(study), colspan=3)
# add headers
cells = ['Risk of Bias', 'Rating', 'Rating rationale']
for i, cell in enumerate(cells):
tbl.new_th(1, i, cell)
# add body
row = 2
for d in study["qualities"]:
tbl.new_td_txt(row, 0, d["metric"]["metric"])
tbl.new_td_txt(row, 1, d["score_symbol"], shade=d["score_shade"])
tbl.new_td_txt(row, 2, strip_tags(d["notes"]))
row += 1
return tbl.render(self.doc)
def build_ROB_legend(self):
widths = [1.5, 1.0, 0.4, 1.0, 0.4, 1.0, 0.4, 0.4, 1.0, 0.4]
tbl = TableMaker(widths, numHeaders=0, firstRowCaption=False, tblStyle="ntpTbl")
cells = [
{"runs": [TableMaker.new_run("Risk of bias key:", b=True, newline=False)]},
{"text": "Definitely Low"},
{"text": "++", "shade": models.StudyQuality.SCORE_SHADES[4]},
{"text": "Probably Low"},
{"text": "+", "shade": models.StudyQuality.SCORE_SHADES[3]},
{"text": "Probably High"},
{"text": "-", "shade": models.StudyQuality.SCORE_SHADES[2]},
{"text": "NR", "shade": models.StudyQuality.SCORE_SHADES[0]},
{"text": "Definitely High"},
{"text": "--", "shade": models.StudyQuality.SCORE_SHADES[1]},
]
for i, cell in enumerate(cells):
if "runs" in cell:
tbl.new_td_run(0, i, cell["runs"], shade=cell.get("shade"))
else:
tbl.new_td_txt(0, i, cell["text"], shade=cell.get("shade"))
return tbl.render(self.doc)
def build_ROB_legend(self):
widths = [1.5, 1.0, 0.4, 1.0, 0.4, 1.0, 0.4, 0.4, 1.0, 0.4]
tbl = TableMaker(widths, numHeaders=0, firstRowCaption=False, tblStyle='ntpTbl')
cells = [
{'runs': [TableMaker.new_run('Risk of bias key:', b=True, newline=False)]},
{'text': 'Definitely Low'},
{'text': '++', 'shade': RiskOfBiasScore.SCORE_SHADES[4]},
{'text': 'Probably Low'},
{'text': '+', 'shade': RiskOfBiasScore.SCORE_SHADES[3]},
{'text': 'Probably High'},
{'text': '-', 'shade': RiskOfBiasScore.SCORE_SHADES[2]},
{'text': 'NR', 'shade': RiskOfBiasScore.SCORE_SHADES[0]},
{'text': 'Definitely High'},
{'text': '--', 'shade': RiskOfBiasScore.SCORE_SHADES[1]},
]
for i, cell in enumerate(cells):
if 'runs' in cell:
tbl.new_td_run(0, i, cell['runs'], shade=cell.get('shade'))
else:
tbl.new_td_txt(0, i, cell['text'], shade=cell.get('shade'))
return tbl.render(self.doc)
def _build_study(self, d):
rows = 0
tbl = TableMaker(self.COLUMN_WIDTHS,
numHeaders=0,
firstRowCaption=False,
tblStyle="ntpTbl")
rowspan = 0
for ep in d["results"]:
rowspan += 1
if (len(ep["wrd_incidents"]) > 0
or len(ep["wrd_incidence_significance"]) > 0):
rowspan += 1
if (len(ep["wrd_multiplicities"]) > 0
or len(ep["wrd_multiplicity_significance"]) > 0):
rowspan += 1
if (len(ep["wrd_total_tumours"]) > 0
or len(ep["wrd_total_tumours_significance"]) > 0):
rowspan += 1
# Column A
runs = [
tbl.new_run(d["studyDesign"], b=True),
tbl.new_run(d["species"], b=True, newline=False),
tbl.new_run(f', {d["strain"]} {d["sex"]}'),
tbl.new_run(d["ageAtStart"]),
tbl.new_run(d["duration"]),
tbl.new_run(d["reference"]["name"], newline=False),
]
tbl.new_td_run(rows, 0, runs, rowspan=rowspan)
# Column B
txt = dedent(f"""\
{d["dosingRoute"]}
{d["agent"]}, {d["purity"]}
{d["vehicle"]}
{d["wrd_doses"]}
{d["dosingRegimen"]}
{d["wrd_nStarts"]}
{d["wrd_nSurvivings"]}""")
tbl.new_td_txt(rows, 1, txt, rowspan=rowspan)
# Columns C, D
irows = rows
for ep in d["results"]:
txt = f'{ep["tumourSite"]}: {ep["histology"]}'
runs = [tbl.new_run(txt, b=True, newline=False)]
tbl.new_td_run(irows, 2, runs, colspan=2)
irows += 1
if (len(ep["wrd_incidents"]) > 0
or len(ep["wrd_incidence_significance"]) > 0):
tbl.new_td_txt(irows, 2, ep["wrd_incidents"])
tbl.new_td_txt(irows, 3, ep["wrd_incidence_significance"])
irows += 1
if (len(ep["wrd_multiplicities"]) > 0
or len(ep["wrd_multiplicity_significance"]) > 0):
tbl.new_td_txt(irows, 2, ep["wrd_multiplicities"])
tbl.new_td_txt(irows, 3, ep["wrd_multiplicity_significance"])
irows += 1
if (len(ep["wrd_total_tumours"]) > 0
or len(ep["wrd_total_tumours_significance"]) > 0):
tbl.new_td_txt(irows, 2, ep["wrd_total_tumours"])
tbl.new_td_txt(irows, 3, ep["wrd_total_tumours_significance"])
irows += 1
# Column E
runs = [
tbl.new_run("Principal strengths:", b=True),
tbl.new_run(d["wrd_strengths"]),
tbl.new_run("Principal limitations:", b=True),
tbl.new_run(d["wrd_limitations"]),
tbl.new_run("Other comments:", b=True),
tbl.new_run(d["wrd_comments"], newline=False),
]
tbl.new_td_run(rows, 4, runs, rowspan=rowspan)
return tbl
def _build_study(self, study):
row = 0
tbl = TableMaker(
self.COLUMN_WIDTHS, numHeaders=0, firstRowCaption=False, tblStyle="ntpTbl"
)
sites = self.group_results_by_site(study["results"])
study_rowspan = self.get_study_rowspan(sites)
# Column A
runs = [
tbl.new_run(study["reference"]["name"], b=True),
tbl.new_run("Animal:", b=True),
tbl.new_run(f'{study["species"]} {study["strain"]}'),
tbl.new_run(study["sex"]),
tbl.new_run("Animal age at the beginning of exposure:", b=True),
tbl.new_run(study["ageAtStart"]),
tbl.new_run("Study duration:", b=True),
tbl.new_run(study["duration"], newline=False),
]
tbl.new_td_run(row, 0, runs, rowspan=study_rowspan)
# Column B
runs = [
tbl.new_run("Agent and purity:", b=True),
tbl.new_run(study["agent"]),
tbl.new_run(study["purity"]),
tbl.new_run("Exposure route:", b=True),
tbl.new_run(study["dosingRoute"]),
tbl.new_run("Exposure concentrations, frequency, and duration:", b=True),
tbl.new_run(study["dosingRegimen"], newline=False),
]
tbl.new_td_run(row, 1, runs, rowspan=study_rowspan)
site_row = row
for results in list(sites.values()):
# Column C & D
for result in results:
# save significance footnotes
notes = result.get("significanceNotes", "") or ""
if notes and hash(notes) not in self.footnotes:
self.footnotes[hash(notes)] = notes
# write histology (and footnote adjustment)
txt = result["tumourSite"]
histology = result.get("histology")
if histology:
txt = f"{txt} – {histology}"
if result["adjustedIncidence"]:
txt += "ᵃ"
fn = "ᵃ Adjusted percent incidence based on Poly-3 estimated neoplasm incidence after adjustment for intercurrent mortality." # noqa
self.footnotes[hash(fn)] = fn
runs = [tbl.new_run(txt, b=True, newline=False)]
tbl.new_td_run(site_row, 2, runs, colspan=2)
site_row += 1
# write groups
for group in result["endpointGroups"]:
dose = group["dose"]
tbl.new_td_txt(site_row, 2, dose)
txt = group.get("incidence", "")
val = group.get("incidenceSymbol", None)
if val:
txt += val
val = group.get("incidencePercent", "None")
if val:
txt += f" ({val}%)"
tbl.new_td_txt(site_row, 3, txt)
site_row += 1
# write trend test
txt = result.get("trendTest")
if txt:
# italicize 'P' in 'P-value'
runs = [
tbl.new_run("Trend ", newline=False),
tbl.new_run("P", i=True, newline=False),
tbl.new_run(f"-value: {txt}"),
]
tbl.new_td_run(site_row, 2, runs, colspan=2)
site_row += 1
# Column E
first_result = study["results"][0] if len(study["results"]) > 0 else {}
# blank runs are extra newlines as requested by report writers
runs = [
tbl.new_run("Survival: ", b=True, newline=False),
tbl.new_run(first_result["survivalNotes"] or ""),
tbl.new_run(""),
tbl.new_run("Body weight: ", b=True, newline=False),
tbl.new_run(first_result["bodyWeightNotes"] or ""),
tbl.new_run(""),
tbl.new_run(
"Significantly increased preneoplastic lesions: ", b=True, newline=False
),
tbl.new_run(first_result["nonNeoplasticFindings"] or ""),
tbl.new_run(""),
tbl.new_run("Other comments: ", b=True, newline=False),
tbl.new_run(first_result["comments"] or ""),
tbl.new_run(""),
tbl.new_run(
f'Overall utility: [{study["overallUtility"]}] ', b=True, newline=False
),
tbl.new_run(study["overallUtilityRationale"] or ""),
]
tbl.new_td_run(row, 4, runs, rowspan=study_rowspan)
return tbl
def _build_study(self, study):
row = 0
tbl = TableMaker(self.COLUMN_WIDTHS,
numHeaders=0,
firstRowCaption=False,
tblStyle="ntpTbl")
sites = self.group_results_by_site(study["results"])
study_rowspan = self.get_study_rowspan(sites)
# Column A
runs = [
tbl.new_run(study["reference"]["name"], b=True),
tbl.new_run("Animal:", b=True),
tbl.new_run(f'{study["species"]} {study["strain"]}'),
tbl.new_run(study["sex"]),
tbl.new_run("Animal age at the beginning of exposure:", b=True),
tbl.new_run(study["ageAtStart"]),
tbl.new_run("Study duration:", b=True),
tbl.new_run(study["duration"], newline=False),
]
tbl.new_td_run(row, 0, runs, rowspan=study_rowspan)
# Column B
runs = [
tbl.new_run("Agent and purity:", b=True),
tbl.new_run(study["agent"]),
tbl.new_run(study["purity"]),
tbl.new_run("Exposure route:", b=True),
tbl.new_run(study["dosingRoute"]),
tbl.new_run("Exposure concentrations, frequency, and duration:",
b=True),
tbl.new_run(study["dosingRegimen"], newline=False),
]
tbl.new_td_run(row, 1, runs, rowspan=study_rowspan)
site_row = row
for results in list(sites.values()):
# Column C & D
for result in results:
# save significance footnotes
notes = result.get("significanceNotes", "") or ""
if notes and hash(notes) not in self.footnotes:
self.footnotes[hash(notes)] = notes
# write histology (and footnote adjustment)
txt = result["tumourSite"]
histology = result.get("histology")
if histology:
txt = f"{txt} – {histology}"
if result["adjustedIncidence"]:
txt += "ᵃ"
fn = "ᵃ Adjusted percent incidence based on Poly-3 estimated neoplasm incidence after adjustment for intercurrent mortality." # noqa
self.footnotes[hash(fn)] = fn
runs = [tbl.new_run(txt, b=True, newline=False)]
tbl.new_td_run(site_row, 2, runs, colspan=2)
site_row += 1
# write groups
for group in result["endpointGroups"]:
dose = group["dose"]
tbl.new_td_txt(site_row, 2, dose)
txt = group.get("incidence", "")
val = group.get("incidenceSymbol", None)
if val:
txt += val
val = group.get("incidencePercent", "None")
if val:
txt += f" ({val}%)"
tbl.new_td_txt(site_row, 3, txt)
site_row += 1
# write trend test
txt = result.get("trendTest")
if txt:
# italicize 'P' in 'P-value'
runs = [
tbl.new_run("Trend ", newline=False),
tbl.new_run("P", i=True, newline=False),
tbl.new_run(f"-value: {txt}"),
]
tbl.new_td_run(site_row, 2, runs, colspan=2)
site_row += 1
# Column E
first_result = study["results"][0] if len(study["results"]) > 0 else {}
# blank runs are extra newlines as requested by report writers
runs = [
tbl.new_run("Survival: ", b=True, newline=False),
tbl.new_run(first_result["survivalNotes"] or ""),
tbl.new_run(""),
tbl.new_run("Body weight: ", b=True, newline=False),
tbl.new_run(first_result["bodyWeightNotes"] or ""),
tbl.new_run(""),
tbl.new_run("Significantly increased preneoplastic lesions: ",
b=True,
newline=False),
tbl.new_run(first_result["nonNeoplasticFindings"] or ""),
tbl.new_run(""),
tbl.new_run("Other comments: ", b=True, newline=False),
tbl.new_run(first_result["comments"] or ""),
tbl.new_run(""),
tbl.new_run(f'Overall utility: [{study["overallUtility"]}] ',
b=True,
newline=False),
tbl.new_run(study["overallUtilityRationale"] or ""),
]
tbl.new_td_run(row, 4, runs, rowspan=study_rowspan)
return tbl
def _build_description(self, desc, site):
results = desc["organSiteResults"][site]
row = 0
tbl = TableMaker(self.COLUMN_WIDTHS,
numHeaders=0,
firstRowCaption=False,
tblStyle="ntpTbl")
rowspan = self.get_desc_rowspan(results)
# Column A
runs = [
tbl.new_run(desc["reference"]["name"]),
tbl.new_run(desc["studyDesign"]),
tbl.new_run(to_string(desc, "location")),
tbl.new_run("Enrollment or follow-up:", b=True),
tbl.new_run(to_string(desc, "enrollmentDates")),
]
tbl.new_td_run(row, 0, runs, rowspan=rowspan)
# Column B
if desc["isCaseControl"]:
txt = (f'Cases: {desc.get("populationSizeCases", "")};'
f' Controls: {desc.get("populationSizeControls", "")}')
descriptors = desc.get("otherPopulationDescriptors")
if descriptors is not None:
txt = dedent(f"""\
{descriptors}
{txt}""")
popD = tbl.new_run(txt)
else:
popD = tbl.new_run(
dedent(f"""\
{desc.get("otherPopulationDescriptors", "")}
{desc.get("cohortPopulationSize", "")}"""))
runs = [
tbl.new_run("Population:", b=True),
popD,
tbl.new_run("Exposure assessment method: ", b=True, newline=False),
tbl.new_run(desc["exposureAssessmentType"], newline=False),
]
tbl.new_td_run(row, 1, runs, rowspan=rowspan)
result_row = row
for res in results:
# Columns C,D
result_rowspan = self.get_result_rowspan(res)
risk_row = result_row
if res.get("organSite") or res.get("effectMeasure"):
txt = str(res["organSite"]) if res.get("organSite") else ""
if res.get("effectMeasure"):
txt += ": " if len(txt) > 0 else ""
txt += res["effectMeasure"]
if res.get("effectUnits") is not None:
txt += f' {res["effectUnits"]}'
run = [tbl.new_run(txt, b=True, newline=False)]
tbl.new_td_run(risk_row, 2, run, colspan=2)
risk_row += 1
for est in res["riskEstimates"]:
tbl.new_td_txt(risk_row, 2, est["exposureCategory"])
tbl.new_td_txt(risk_row, 3, est["riskFormatted"])
risk_row += 1
if res.get("trendTest"):
run = [
tbl.new_run("Trend-test ", newline=False),
tbl.new_run("p", i=True, newline=False),
tbl.new_run("-value: ", newline=False),
tbl.new_run(res["trendTest"], newline=False),
]
tbl.new_td_run(risk_row, 2, run, colspan=2)
risk_row += 1
# Column E
tbl.new_td_txt(result_row,
4,
res["wrd_covariatesList"],
rowspan=result_rowspan)
# update result_row
result_row = result_row + result_rowspan
# Column F
runs = []
wrd_notes = desc.get("wrd_notes")
if wrd_notes is not None and wrd_notes != "":
runs.append(tbl.new_run(wrd_notes))
additional_results = "\n".join([
res["additionalResults"] for res in results
if ("additionalResults" in res and res["additionalResults"]
is not None and res["additionalResults"] is not "")
])
if additional_results is "":
additional_results = "-"
runs.extend([
tbl.new_run("Exposure information:", b=True),
tbl.new_run(to_string(desc, "exposureLevel")),
tbl.new_run("Strengths:", b=True),
tbl.new_run(to_string(desc, "strengths")),
tbl.new_run("Limitations:", b=True),
tbl.new_run(to_string(desc, "limitations")),
tbl.new_run("Additional results:", b=True),
tbl.new_run(additional_results),
tbl.new_run("Confidence in evidence:", b=True),
tbl.new_run(to_string(desc, "confidenceInEvidence"),
newline=False),
])
tbl.new_td_run(row, 5, runs, rowspan=rowspan)
return tbl
def _build_description(self, desc, site):
results = desc["organSiteResults"][site]
row = 0
tbl = TableMaker(
self.COLUMN_WIDTHS, numHeaders=0, firstRowCaption=False, tblStyle="ntpTbl"
)
rowspan = self.get_desc_rowspan(results)
# Column A
runs = [
tbl.new_run(desc["reference"]["name"]),
tbl.new_run(desc["studyDesign"]),
tbl.new_run(to_string(desc, "location")),
tbl.new_run("Enrollment or follow-up:", b=True),
tbl.new_run(to_string(desc, "enrollmentDates")),
]
tbl.new_td_run(row, 0, runs, rowspan=rowspan)
# Column B
if desc["isCaseControl"]:
txt = (
f'Cases: {desc.get("populationSizeCases", "")};'
f' Controls: {desc.get("populationSizeControls", "")}'
)
descriptors = desc.get("otherPopulationDescriptors")
if descriptors is not None:
txt = dedent(
f"""\
{descriptors}
{txt}"""
)
popD = tbl.new_run(txt)
else:
popD = tbl.new_run(
dedent(
f"""\
{desc.get("otherPopulationDescriptors", "")}
{desc.get("cohortPopulationSize", "")}"""
)
)
runs = [
tbl.new_run("Population:", b=True),
popD,
tbl.new_run("Exposure assessment method: ", b=True, newline=False),
tbl.new_run(desc["exposureAssessmentType"], newline=False),
]
tbl.new_td_run(row, 1, runs, rowspan=rowspan)
result_row = row
for res in results:
# Columns C,D
result_rowspan = self.get_result_rowspan(res)
risk_row = result_row
if res.get("organSite") or res.get("effectMeasure"):
txt = str(res["organSite"]) if res.get("organSite") else ""
if res.get("effectMeasure"):
txt += ": " if len(txt) > 0 else ""
txt += res["effectMeasure"]
if res.get("effectUnits") is not None:
txt += f' {res["effectUnits"]}'
run = [tbl.new_run(txt, b=True, newline=False)]
tbl.new_td_run(risk_row, 2, run, colspan=2)
risk_row += 1
for est in res["riskEstimates"]:
tbl.new_td_txt(risk_row, 2, est["exposureCategory"])
tbl.new_td_txt(risk_row, 3, est["riskFormatted"])
risk_row += 1
if res.get("trendTest"):
run = [
tbl.new_run("Trend-test ", newline=False),
tbl.new_run("p", i=True, newline=False),
tbl.new_run("-value: ", newline=False),
tbl.new_run(res["trendTest"], newline=False),
]
tbl.new_td_run(risk_row, 2, run, colspan=2)
risk_row += 1
# Column E
tbl.new_td_txt(
result_row, 4, res["wrd_covariatesList"], rowspan=result_rowspan
)
# update result_row
result_row = result_row + result_rowspan
# Column F
runs = []
wrd_notes = desc.get("wrd_notes")
if wrd_notes is not None and wrd_notes != "":
runs.append(tbl.new_run(wrd_notes))
additional_results = "\n".join(
[
res["additionalResults"]
for res in results
if (
"additionalResults" in res
and res["additionalResults"] is not None
and res["additionalResults"] is not ""
)
]
)
if additional_results is "":
additional_results = "-"
runs.extend(
[
tbl.new_run("Exposure information:", b=True),
tbl.new_run(to_string(desc, "exposureLevel")),
tbl.new_run("Strengths:", b=True),
tbl.new_run(to_string(desc, "strengths")),
tbl.new_run("Limitations:", b=True),
tbl.new_run(to_string(desc, "limitations")),
tbl.new_run("Additional results:", b=True),
tbl.new_run(additional_results),
tbl.new_run("Confidence in evidence:", b=True),
tbl.new_run(to_string(desc, "confidenceInEvidence"), newline=False),
]
)
tbl.new_td_run(row, 5, runs, rowspan=rowspan)
return tbl
def _build_result(self, res):
rows = 0
tbl = TableMaker(
self.COLUMN_WIDTHS, numHeaders=0, firstRowCaption=False, tblStyle="ntpTbl"
)
rowspan = len(res["riskEstimates"])
# Column A
txt = dedent(
f"""\
{res["descriptive"]["reference"]["name"]}
{res["descriptive"]["studyDesign"]}
{res["descriptive"]["location"]}
{res["descriptive"]["enrollmentDates"]}"""
)
tbl.new_td_txt(rows, 0, txt, rowspan=rowspan)
# Column B
if res["descriptive"]["isCaseControl"]:
popD = tbl.new_run(
dedent(
f"""\
{res["descriptive"].get("eligibilityCriteria", "")}
Cases: {res["descriptive"].get("populationSizeCase", "")}
Controls: {res["descriptive"].get("populationSizeControl", "")}"""
)
)
else:
popD = tbl.new_run(
dedent(
f"""\
{res["descriptive"].get("eligibilityCriteria", "")}
{res["descriptive"].get("populationSize", "")}"""
)
)
runs = [
popD,
tbl.new_run("Exposure assessment method: ", b=True, newline=False),
tbl.new_run(res["descriptive"]["exposureAssessmentType"], newline=False),
]
if res.get("organSite"):
runs.insert(0, tbl.new_run(res["organSite"], b=True))
tbl.new_td_run(rows, 1, runs, rowspan=rowspan)
# Columns C,D,E
for i, est in enumerate(res["riskEstimates"]):
tbl.new_td_txt(rows + i, 2, est["exposureCategory"])
tbl.new_td_txt(rows + i, 3, est["numberExposed"])
tbl.new_td_txt(rows + i, 4, est["riskFormatted"])
# Column F
txt = res["wrd_covariatesList"]
runs = [tbl.new_run(res["wrd_covariatesList"])]
if res["hasTrendTest"]:
runs.extend(
[
tbl.new_run("Trend-test ", newline=False),
tbl.new_run("P", i=True, newline=False),
tbl.new_run(f'-value: {res["trendTest"]}', newline=False),
]
)
tbl.new_td_run(rows, 5, runs, rowspan=rowspan)
# Column G
runs = [
tbl.new_run(res["descriptive"]["wrd_notes"]),
tbl.new_run("Strengths:", b=True),
tbl.new_run(res["descriptive"]["strengths"]),
tbl.new_run("Limitations:", b=True),
tbl.new_run(res["descriptive"]["limitations"], newline=False),
]
tbl.new_td_run(rows, 6, runs, rowspan=rowspan)
return tbl
def _build_description(self, d):
rows = 0
tbl = TableMaker(
self.COLUMN_WIDTHS, numHeaders=0, firstRowCaption=False, tblStyle="ntpTbl"
)
for res in d["results"]:
res["_rowspan"] = max(len(res["riskEstimates"]), 1)
effectUnits = res.get("effectUnits", None)
if effectUnits and len(effectUnits) > 0:
res["_rowspan"] += 1
if res["hasTrendTest"]:
res["_rowspan"] += 1
st_rowspan = sum(res["_rowspan"] for res in d["results"])
# Column A
runs = [
tbl.new_run(d["reference"]["name"]),
tbl.new_run(d["location"]),
tbl.new_run(d["enrollmentDates"]),
tbl.new_run(d["studyDesign"]),
]
tbl.new_td_run(rows, 0, runs, rowspan=st_rowspan)
# Column B
runs = self.getCol2(d, tbl)
tbl.new_td_run(rows, 1, runs, rowspan=st_rowspan)
# Columns C, D, E, F, G
irows = rows
for res in d["results"]:
additionalRows = 0
tbl.new_td_txt(irows, 2, res["printOrganSite"], rowspan=res["_rowspan"])
tbl.new_td_txt(irows, 6, res["wrd_covariatesList"], rowspan=res["_rowspan"])
effectUnits = res.get("effectUnits", None)
if effectUnits and len(effectUnits) > 0:
tbl.new_td_txt(irows, 3, effectUnits, colspan=3)
additionalRows += 1
stratum = res.get("stratum", None)
for i, est in enumerate(res["riskEstimates"]):
runs = []
if i == 0 and stratum and len(stratum) > 0:
runs.append(tbl.new_run(f"{stratum}: ", b=True, newline=False))
runs.append(tbl.new_run(est["exposureCategory"], newline=False))
tbl.new_td_run(irows + additionalRows, 3, runs)
tbl.new_td_txt(irows + additionalRows, 4, est["numberExposed"])
tbl.new_td_txt(irows + additionalRows, 5, est["riskFormatted"])
additionalRows += 1
if res["hasTrendTest"]:
txt = f'Trend-test p-value: {res["trendTest"]}'
tbl.new_td_txt(irows + additionalRows, 3, txt, colspan=3)
additionalRows += 1
irows += additionalRows
# Column H
runs = [
tbl.new_run(d["wrd_notes"]),
tbl.new_run("Strengths: ", b=True, newline=False),
tbl.new_run(d.get("strengths", "")),
tbl.new_run("Limitations: ", b=True, newline=False),
tbl.new_run(d.get("limitations", "")),
]
tbl.new_td_run(rows, 7, runs, rowspan=st_rowspan)
return tbl
def _build_study(self, d):
rows = 0
tbl = TableMaker(
self.COLUMN_WIDTHS, numHeaders=0, firstRowCaption=False, tblStyle="ntpTbl"
)
rowspan = 0
for ep in d["results"]:
rowspan += 1
if (
len(ep["wrd_incidents"]) > 0
or len(ep["wrd_incidence_significance"]) > 0
):
rowspan += 1
if (
len(ep["wrd_multiplicities"]) > 0
or len(ep["wrd_multiplicity_significance"]) > 0
):
rowspan += 1
if (
len(ep["wrd_total_tumours"]) > 0
or len(ep["wrd_total_tumours_significance"]) > 0
):
rowspan += 1
# Column A
runs = [
tbl.new_run(d["studyDesign"], b=True),
tbl.new_run(d["species"], b=True, newline=False),
tbl.new_run(f', {d["strain"]} {d["sex"]}'),
tbl.new_run(d["ageAtStart"]),
tbl.new_run(d["duration"]),
tbl.new_run(d["reference"]["name"], newline=False),
]
tbl.new_td_run(rows, 0, runs, rowspan=rowspan)
# Column B
txt = dedent(
f"""\
{d["dosingRoute"]}
{d["agent"]}, {d["purity"]}
{d["vehicle"]}
{d["wrd_doses"]}
{d["dosingRegimen"]}
{d["wrd_nStarts"]}
{d["wrd_nSurvivings"]}"""
)
tbl.new_td_txt(rows, 1, txt, rowspan=rowspan)
# Columns C, D
irows = rows
for ep in d["results"]:
txt = f'{ep["tumourSite"]}: {ep["histology"]}'
runs = [tbl.new_run(txt, b=True, newline=False)]
tbl.new_td_run(irows, 2, runs, colspan=2)
irows += 1
if (
len(ep["wrd_incidents"]) > 0
or len(ep["wrd_incidence_significance"]) > 0
):
tbl.new_td_txt(irows, 2, ep["wrd_incidents"])
tbl.new_td_txt(irows, 3, ep["wrd_incidence_significance"])
irows += 1
if (
len(ep["wrd_multiplicities"]) > 0
or len(ep["wrd_multiplicity_significance"]) > 0
):
tbl.new_td_txt(irows, 2, ep["wrd_multiplicities"])
tbl.new_td_txt(irows, 3, ep["wrd_multiplicity_significance"])
irows += 1
if (
len(ep["wrd_total_tumours"]) > 0
or len(ep["wrd_total_tumours_significance"]) > 0
):
tbl.new_td_txt(irows, 2, ep["wrd_total_tumours"])
tbl.new_td_txt(irows, 3, ep["wrd_total_tumours_significance"])
irows += 1
# Column E
runs = [
tbl.new_run("Principal strengths:", b=True),
tbl.new_run(d["wrd_strengths"]),
tbl.new_run("Principal limitations:", b=True),
tbl.new_run(d["wrd_limitations"]),
tbl.new_run("Other comments:", b=True),
tbl.new_run(d["wrd_comments"], newline=False),
]
tbl.new_td_run(rows, 4, runs, rowspan=rowspan)
return tbl
def build_summary_table(self, ag):
widths = [2.5, 2.7, 1.5, 0.5, 1.2, 1.6]
tbl = TableMaker(widths, numHeaders=3, firstRowCaption=True, tblStyle="ntpTbl")
doses = self.getDoses(ag)
firstDose = doses.keys()[0]
# add caption
tbl.new_td_txt(0, 0, ag['name'], colspan=6)
# add headers
tbl.new_th(1, 0, r'Reference, Animal Model, and Dosing', rowspan=2)
tbl.new_th(1, 1, r'Health outcome', rowspan=2)
tbl.new_th(1, 2, r'Results', colspan=4)
tbl.new_th(2, 2, u'Dose ({0})'.format(firstDose))
tbl.new_th(2, 3, r'N')
tbl.new_th(2, 4, u'Mean \u00B1 SD',)
tbl.new_th(2, 5, r'% control (95% CI)')
# build endpoint rows
rows = 3
for ep in ag["eps"]:
if ep["data_extracted"] == False:
continue
# add endpoint-name as first-row
tbl.new_td_run(
rows, 2,
[TableMaker.new_run(ep["name"], i=True, newline=False)],
colspan=4
)
rows += 1
# build column-header table
data_type = ep["data_type"]
for i, eg in enumerate(ep["groups"]):
ci = []
resp = u"-"
if data_type == "C":
resp = unicode(eg.get("response", "-"))
if eg.get('stdev') is not None:
resp += u' \u00B1 {0:.2f}'.format(eg["stdev"])
elif data_type in ["D", "DC"]:
if eg.get("incidence") is not None:
resp = u'{0}/{1}'.format(eg["incidence"], eg["n"])
ci.append(u"NR")
else: # ["P"]
resp = u'NR'
if i != 0 and eg.get("percentControlMean") is not None:
ci.append("{0:.1f}".format(eg["percentControlMean"]))
if eg.get("percentControlLow") is not None and eg.get("percentControlHigh") is not None:
ci.append(u'({0:.1f}, {1:.1f})'.format(eg["percentControlLow"], eg["percentControlHigh"]))
ci = u" ".join(ci)
tbl.new_td_txt(rows, 2, unicode(doses[firstDose][i]))
tbl.new_td_txt(rows, 3, unicode(eg.get("n", "-")))
tbl.new_td_txt(rows, 4, resp)
tbl.new_td_txt(rows, 5, ci)
rows += 1
else:
# add blank cells if none-exist
tbl.new_td_txt(3, 2, "")
tbl.new_td_txt(3, 3, "")
tbl.new_td_txt(3, 4, "")
tbl.new_td_txt(3, 5, "")
# adjust rowspans for summary rows
rowspan = rows - 3
tbl.new_td_run(3, 0, self.getStudyDetailsRuns(ag, doses), rowspan=rowspan)
tbl.new_td_run(3, 1, self.getHealthOutcomes(ag), rowspan=rowspan)
tbl.render(self.doc)
self.doc.add_paragraph("\n")