def get_task_html(self):
h = self.get_standard_clinician_block() + u"""
<div class="summary">
<table class="summary">
""" + self.get_is_complete_tr()
h += tr_qa("Total errors", self.n_errors())
h += tr_qa("Predicted full-scale IQ <sup>[1]</sup>", self.fsiq())
h += tr_qa("Predicted verbal IQ <sup>[2]</sup>", self.viq())
h += tr_qa("Predicted performance IQ <sup>[3]</sup>", self.piq())
h += u"""
</table>
</div>
<div class="explanation">
Estimates premorbid IQ by pronunciation of irregular words.
</div>
<table class="taskdetail">
<tr>
<th width="16%">Word</th><th width="16%">Correct?</th>
<th width="16%">Word</th><th width="16%">Correct?</th>
<th width="16%">Word</th><th width="16%">Correct?</th>
</tr>
"""
nwords = len(WORDLIST)
ncolumns = 3
nrows = int(math.ceil(float(nwords)/float(ncolumns)))
column = 0
row = 0
# x: word index (shown in top-to-bottom then left-to-right sequence)
for unused_loopvar in range(nwords):
x = (column * nrows) + row
if column == 0: # first column
h += u"<tr>"
h += td(ACCENTED_WORDLIST[x])
h += td(answer(getattr(self, WORDLIST[x])))
if column == (ncolumns - 1): # last column
h += u"</tr>"
row += 1
column = (column + 1) % ncolumns
h += u"""
</table>
<div class="footnotes">
[1] Full-scale IQ ≈ 127.7 – 0.826 × errors.
[2] Verbal IQ ≈ 129.0 – 0.919 × errors.
[3] Performance IQ ≈ 123.5 – 0.645 × errors.
</div>
<div class="copyright">
NART: Copyright © Hazel E. Nelson. Used with permission.
</div>
"""
return h
def get_html_correct_by_group_and_block(self, trialarray):
if not trialarray:
return div(italic("No trials"))
html = """
<table class="extradetail">
<tr>
<th>Block</th>
"""
for g in range(N_CUES):
html += """
<th>Group {0} P(detected|present)</th>
<th>Group {0} P(detected|absent)</th>
<th>Group {0} c</th>
<th>Group {0} d'</th>
""".format(g)
html += """
</th>
</tr>
"""
for b in range(self.num_blocks):
html += "<tr>" + td(b)
for g in range(N_CUES):
(p_detected_given_present,
p_detected_given_absent,
c,
dprime,
n_trials) = self.get_p_detected(trialarray, [b], [g])
html += td(a(p_detected_given_present))
html += td(a(p_detected_given_absent))
html += td(a(c))
html += td(a(dprime))
html += "</tr>\n"
html += """
</table>
"""
return html
def get_task_html(self):
DICTQ1 = {None: None}
DICTQ3 = {None: None}
DICTQ4 = {None: None}
DICTQ5 = {None: None}
DICTQ11 = {None: None}
DICTQ12 = {None: None}
for option in range(1, 5):
DICTQ1[option] = WSTRING("gmcpq_q1_option" + str(option))
for option in range(1, 6):
DICTQ3[option] = WSTRING("gmcpq_q3_option" + str(option))
DICTQ11[option] = WSTRING("gmcpq_q11_option" + str(option))
for option in range(0, 6):
prefix = str(option) + u" – " if option > 0 else ""
DICTQ4[option] = prefix + WSTRING("gmcpq_q4_option" + str(option))
DICTQ5[option] = prefix + WSTRING("gmcpq_q5_option" + str(option))
for option in range(1, 17):
DICTQ12[option] = WSTRING("gmcpq_ethnicity_option" + str(option))
h = u"""
<div class="summary">
<table class="summary">
{}
</table>
</div>
<table class="taskdetail">
<tr>
<th width="60%">Question</th>
<th width="40%">Answer</th>
</tr>
""".format(self.get_is_complete_tr())
ell = "… " # horizontal ellipsis
sep_row = subheading_spanning_two_columns("")
blank_cell = td("", td_class="subheading")
h += tr_qa(WSTRING("gmcpq_q_doctor"), ws.webify(self.doctor))
h += sep_row
h += tr_qa(WSTRING("gmcpq_q1"), get_from_dict(DICTQ1, self.q1))
h += tr(td(WSTRING("gmcpq_q2")), blank_cell, literal=True)
h += tr_qa(ell + WSTRING("gmcpq_q2_a"), get_yes_no_none(self.q2a))
h += tr_qa(ell + WSTRING("gmcpq_q2_b"), get_yes_no_none(self.q2b))
h += tr_qa(ell + WSTRING("gmcpq_q2_c"), get_yes_no_none(self.q2c))
h += tr_qa(ell + WSTRING("gmcpq_q2_d"), get_yes_no_none(self.q2d))
h += tr_qa(ell + WSTRING("gmcpq_q2_e"), get_yes_no_none(self.q2e))
h += tr_qa(ell + WSTRING("gmcpq_q2_f"), get_yes_no_none(self.q2f))
h += tr_qa(ell + ell + WSTRING("gmcpq_q2f_s"),
ws.webify(self.q2f_details))
h += tr_qa(WSTRING("gmcpq_q3"), get_from_dict(DICTQ3, self.q3))
h += tr(td(WSTRING("gmcpq_q4")), blank_cell, literal=True)
h += tr_qa(ell + WSTRING("gmcpq_q4_a"),
get_from_dict(DICTQ4, self.q4a))
h += tr_qa(ell + WSTRING("gmcpq_q4_b"),
get_from_dict(DICTQ4, self.q4b))
h += tr_qa(ell + WSTRING("gmcpq_q4_c"),
get_from_dict(DICTQ4, self.q4c))
h += tr_qa(ell + WSTRING("gmcpq_q4_d"),
get_from_dict(DICTQ4, self.q4d))
h += tr_qa(ell + WSTRING("gmcpq_q4_e"),
get_from_dict(DICTQ4, self.q4e))
h += tr_qa(ell + WSTRING("gmcpq_q4_f"),
get_from_dict(DICTQ4, self.q4f))
h += tr_qa(ell + WSTRING("gmcpq_q4_g"),
get_from_dict(DICTQ4, self.q4g))
h += tr(td(WSTRING("gmcpq_q5")), blank_cell, literal=True)
h += tr_qa(ell + WSTRING("gmcpq_q5_a"),
get_from_dict(DICTQ5, self.q5a))
h += tr_qa(ell + WSTRING("gmcpq_q5_b"),
get_from_dict(DICTQ5, self.q5b))
h += tr_qa(WSTRING("gmcpq_q6"), get_yes_no_none(self.q6))
h += tr_qa(WSTRING("gmcpq_q7"), get_yes_no_none(self.q7))
h += tr_qa(WSTRING("gmcpq_q8"), get_yes_no_none(self.q8))
h += tr_qa(WSTRING("gmcpq_q9_s"), ws.webify(self.q9))
h += sep_row
h += tr_qa(WSTRING("sex"), ws.webify(self.q10))
h += tr_qa(WSTRING("gmcpq_q11"), get_from_dict(DICTQ11, self.q11))
h += tr_qa(WSTRING("gmcpq_q12"), get_from_dict(DICTQ12, self.q12))
h += tr_qa(ell + WSTRING("gmcpq_ethnicity_other_s"),
ws.webify(self.q12_details))
h += u"""
</table>
"""
return h
def get_task_html(self):
vsp = self.score_vsp()
naming = self.score_naming()
attention = self.score_attention()
language = self.score_language()
abstraction = self.score_abstraction()
memory = self.score_memory()
orientation = self.score_orientation()
totalscore = self.total_score()
category = self.category()
h = self.get_standard_clinician_block(True, self.comments) + u"""
<div class="summary">
<table class="summary">
""" + self.get_is_complete_tr()
h += tr(WSTRING("total_score"), answer(totalscore) + " / 30")
h += tr_qa(WSTRING("moca_category") + " <sup>[1]</sup>",
category)
h += u"""
</table>
</div>
<table class="taskdetail">
<tr>
<th width="69%">Question</th>
<th width="31%">Score</th>
</tr>
"""
h += tr(WSTRING("moca_subscore_visuospatial"),
answer(vsp) + " / 5",
tr_class="subheading")
h += tr("Path, cube, clock/contour, clock/numbers, clock/hands",
", ".join([answer(x) for x in [self.q1, self.q2, self.q3,
self.q4, self.q5]]))
h += tr(WSTRING("moca_subscore_naming"),
answer(naming) + " / 3",
tr_class="subheading")
h += tr("Lion, rhino, camel",
", ".join([answer(x) for x in [self.q6, self.q7, self.q8]]))
h += tr(WSTRING("moca_subscore_attention"),
answer(attention) + " / 6",
tr_class="subheading")
h += tr("5 digits forwards, 3 digits backwards, tapping, serial 7s "
"[<i>scores 3</i>]",
", ".join([answer(x) for x in [self.q9, self.q10, self.q11,
self.q12]]))
h += tr(WSTRING("moca_subscore_language"),
answer(language) + " / 3",
tr_class="subheading")
h += tr(u"Repeat sentence 1, repeat sentence 2, fluency to letter ‘F’",
", ".join([answer(x) for x in [self.q13, self.q14, self.q15]]))
h += tr(WSTRING("moca_subscore_abstraction"),
answer(abstraction) + " / 2",
tr_class="subheading")
h += tr("Means of transportation, measuring instruments",
", ".join([answer(x) for x in [self.q16, self.q17]]))
h += tr(WSTRING("moca_subscore_memory"),
answer(memory) + " / 5",
tr_class="subheading")
h += tr(
"Registered on first trial [<i>not scored</i>]",
", ".join([
answer(x, formatter_answer=italic)
for x in [
self.register_trial1_1,
self.register_trial1_2,
self.register_trial1_3,
self.register_trial1_4,
self.register_trial1_5
]
])
)
h += tr(
"Registered on second trial [<i>not scored</i>]",
", ".join([
answer(x, formatter_answer=italic)
for x in [
self.register_trial2_1,
self.register_trial2_2,
self.register_trial2_3,
self.register_trial2_4,
self.register_trial2_5
]
])
)
h += tr(
"Recall FACE, VELVET, CHURCH, DAISY, RED with no cue",
", ".join([
answer(x) for x in [
self.q18, self.q19, self.q20, self.q21, self.q22
]
])
)
h += tr(
"Recall with category cue [<i>not scored</i>]",
", ".join([
answer(x, formatter_answer=italic)
for x in [
self.recall_category_cue_1,
self.recall_category_cue_2,
self.recall_category_cue_3,
self.recall_category_cue_4,
self.recall_category_cue_5
]
])
)
h += tr(
"Recall with multiple-choice cue [<i>not scored</i>]",
", ".join([
answer(x, formatter_answer=italic)
for x in [
self.recall_mc_cue_1,
self.recall_mc_cue_2,
self.recall_mc_cue_3,
self.recall_mc_cue_4,
self.recall_mc_cue_5
]
])
)
h += tr(WSTRING("moca_subscore_orientation"),
answer(orientation) + " / 6",
tr_class="subheading")
h += tr(
"Date, month, year, day, place, city",
", ".join([
answer(x) for x in [
self.q23, self.q24, self.q25, self.q26, self.q27, self.q28
]
])
)
h += subheading_spanning_two_columns(WSTRING("moca_education_s"))
h += tr_qa(u"≤12 years’ education?", self.education12y_or_less)
h += u"""
</table>
<table class="taskdetail">
"""
h += subheading_spanning_two_columns(
"Images of tests: trail, cube, clock",
th_not_td=True)
h += tr(
td(self.get_blob_png_html(self.trailpicture_blobid),
td_class="photo", td_width="50%"),
td(self.get_blob_png_html(self.cubepicture_blobid),
td_class="photo", td_width="50%"),
literal=True,
)
h += tr(
td(self.get_blob_png_html(self.trailpicture_blobid),
td_class="photo", td_width="50%"),
td("", td_class="subheading"),
literal=True,
)
h += u"""
</table>
<div class="footnotes">
[1] Normal is ≥26 (Nasreddine et al. 2005, PubMed ID 15817019).
</div>
<div class="copyright">
MoCA: Copyright © Ziad Nasreddine.
May be reproduced without permission for CLINICAL and
EDUCATIONAL use. You must obtain permission from the copyright
holder for any other use.
</div>
"""
return h
def text_row(self, wstringname):
return tr(td(WSTRING(wstringname)),
td("", td_class="subheading"),
literal=True)
def get_task_html(self):
score = self.total_score()
category = self.category()
h = self.get_standard_clinician_block(True, self.comments) + u"""
<div class="summary">
<table class="summary">
""" + self.get_is_complete_tr()
h += tr(WSTRING("total_score"), answer(score) + " / 30")
h += tr_qa(WSTRING("category") + " <sup>[1]</sup>", category)
h += u"""
</table>
</div>
<table class="taskdetail">
<tr>
<th width="80%">Question</th>
<th width="20%">Score</th>
</tr>
"""
h += tr_qa(WSTRING("slums_alert_s"), get_yes_no_none(self.alert))
h += tr_qa(WSTRING("slums_highschool_s"),
get_yes_no_none(self.highschooleducation))
h += tr_qa(WSTRING("slums_q1_s"), self.q1)
h += tr_qa(WSTRING("slums_q2_s"), self.q2)
h += tr_qa(WSTRING("slums_q3_s"), self.q3)
h += tr(u"Q5 <sup>[2]</sup> (money spent, money left "
u"[<i>scores 2</i>]",
u", ".join([answer(x) for x in [self.q5a, self.q5b]]))
h += tr_qa(u"Q6 (animal fluency) [<i>≥15 scores 3, 10–14 scores 2, "
u"5–9 scores 1, 0–4 scores 0</i>]",
self.q6)
h += tr(u"Q7 (recall: apple, pen, tie, house, car)",
u", ".join([answer(x) for x in [self.q7a, self.q7b, self.q7c,
self.q7d, self.q7e]]))
h += tr(u"Q8 (backwards: 648, 8537)",
u", ".join([answer(x) for x in [self.q8b, self.q8c]]))
h += tr(u"Q9 (clock: hour markers, time [<i>score 2 each</i>]",
u", ".join([answer(x) for x in [self.q9a, self.q9b]]))
h += tr(u"Q10 (X in triangle; which is biggest?)",
u", ".join([answer(x) for x in [self.q10a, self.q10b]]))
h += tr(u"Q11 (story: Female’s name? Job? When back to work? "
u"State she lived in? [<i>score 2 each</i>])",
u", ".join([answer(x) for x in [self.q11a, self.q11b,
self.q11c, self.q11d]]))
h += u"""
</table>
<table class="taskdetail">
"""
h += subheading_spanning_two_columns("Images of tests: clock, shapes")
h += tr(
td(self.get_blob_png_html(self.clockpicture_blobid),
td_width="50%", td_class="photo"),
td(self.get_blob_png_html(self.shapespicture_blobid),
td_width="50%", td_class="photo"),
literal=True
)
h += u"""
</table>
<div class="footnotes">
[1] With high school education:
≥27 normal, ≥21 MCI, ≤20 dementia.
Without high school education:
≥25 normal, ≥20 MCI, ≤19 dementia.
(Tariq et al. 2006, PubMed ID 17068312.)
[2] Q4 (learning the five words) isn’t scored.
</div>
"""
return h
