Source code for camcops_server.tasks.nart

#!/usr/bin/env python

"""
camcops_server/tasks/nart.py

===============================================================================

    Copyright (C) 2012-2019 Rudolf Cardinal (rudolf@pobox.com).

    This file is part of CamCOPS.

    CamCOPS is free software: you can redistribute it and/or modify
    it under the terms of the GNU General Public License as published by
    the Free Software Foundation, either version 3 of the License, or
    (at your option) any later version.

    CamCOPS is distributed in the hope that it will be useful,
    but WITHOUT ANY WARRANTY; without even the implied warranty of
    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
    GNU General Public License for more details.

    You should have received a copy of the GNU General Public License
    along with CamCOPS. If not, see <http://www.gnu.org/licenses/>.

===============================================================================

"""

import math
from typing import Any, Dict, List, Optional, Tuple, Type

from sqlalchemy.ext.declarative import DeclarativeMeta
from sqlalchemy.sql.sqltypes import Boolean, Float

from camcops_server.cc_modules.cc_constants import CssClass
from camcops_server.cc_modules.cc_ctvinfo import CTV_INCOMPLETE, CtvInfo
from camcops_server.cc_modules.cc_html import answer, td, tr_qa
from camcops_server.cc_modules.cc_request import CamcopsRequest
from camcops_server.cc_modules.cc_snomed import SnomedExpression, SnomedLookup
from camcops_server.cc_modules.cc_sqla_coltypes import (
    BIT_CHECKER,
    CamcopsColumn,
)
from camcops_server.cc_modules.cc_summaryelement import SummaryElement
from camcops_server.cc_modules.cc_task import (
    Task,
    TaskHasClinicianMixin,
    TaskHasPatientMixin,
)


WORDLIST = [  # Value is true/1 for CORRECT, false/0 for INCORRECT
    "chord",
    "ache",
    "depot",
    "aisle",
    "bouquet",
    "psalm",
    "capon",
    "deny",  # NB reserved word in SQL (auto-handled)
    "nausea",
    "debt",
    "courteous",
    "rarefy",
    "equivocal",
    "naive",  # accent required
    "catacomb",
    "gaoled",
    "thyme",
    "heir",
    "radix",
    "assignate",
    "hiatus",
    "subtle",
    "procreate",
    "gist",
    "gouge",
    "superfluous",
    "simile",
    "banal",
    "quadruped",
    "cellist",
    "facade",  # accent required
    "zealot",
    "drachm",
    "aeon",
    "placebo",
    "abstemious",
    "detente",  # accent required
    "idyll",
    "puerperal",
    "aver",
    "gauche",
    "topiary",
    "leviathan",
    "beatify",
    "prelate",
    "sidereal",
    "demesne",
    "syncope",
    "labile",
    "campanile"
]
ACCENTED_WORDLIST = list(WORDLIST)
# noinspection PyUnresolvedReferences
ACCENTED_WORDLIST[ACCENTED_WORDLIST.index("naive")] = "naïve"
ACCENTED_WORDLIST[ACCENTED_WORDLIST.index("facade")] = "façade"
ACCENTED_WORDLIST[ACCENTED_WORDLIST.index("detente")] = "détente"


# =============================================================================
# NART
# =============================================================================

class NartMetaclass(DeclarativeMeta):
    # noinspection PyInitNewSignature
    def __init__(cls: Type['Nart'],
                 name: str,
                 bases: Tuple[Type, ...],
                 classdict: Dict[str, Any]) -> None:
        for w in WORDLIST:
            setattr(
                cls,
                w,
                CamcopsColumn(
                    w, Boolean,
                    permitted_value_checker=BIT_CHECKER,
                    comment=f"Pronounced {w} correctly (0 no, 1 yes)"
                )
            )
        super().__init__(name, bases, classdict)


[docs]class Nart(TaskHasPatientMixin, TaskHasClinicianMixin, Task, metaclass=NartMetaclass): """ Server implementation of the NART task. """ __tablename__ = "nart" shortname = "NART"
[docs] @staticmethod def longname(req: "CamcopsRequest") -> str: _ = req.gettext return _("National Adult Reading Test")
[docs] def get_clinical_text(self, req: CamcopsRequest) -> List[CtvInfo]: if not self.is_complete(): return CTV_INCOMPLETE return [CtvInfo( content=( "NART predicted WAIS FSIQ {n_fsiq}, WAIS VIQ {n_viq}, " "WAIS PIQ {n_piq}, WAIS-R FSIQ {nw_fsiq}, " "WAIS-IV FSIQ {b_fsiq}, WAIS-IV GAI {b_gai}, " "WAIS-IV VCI {b_vci}, WAIS-IV PRI {b_pri}, " "WAIS_IV WMI {b_wmi}, WAIS-IV PSI {b_psi}".format( n_fsiq=self.nelson_full_scale_iq(), n_viq=self.nelson_verbal_iq(), n_piq=self.nelson_performance_iq(), nw_fsiq=self.nelson_willison_full_scale_iq(), b_fsiq=self.bright_full_scale_iq(), b_gai=self.bright_general_ability(), b_vci=self.bright_verbal_comprehension(), b_pri=self.bright_perceptual_reasoning(), b_wmi=self.bright_working_memory(), b_psi=self.bright_perceptual_speed(), ) ) )]
[docs] def get_summaries(self, req: CamcopsRequest) -> List[SummaryElement]: return self.standard_task_summary_fields() + [ SummaryElement( name="nelson_full_scale_iq", coltype=Float(), value=self.nelson_full_scale_iq(), comment="Predicted WAIS full-scale IQ (Nelson 1982)"), SummaryElement( name="nelson_verbal_iq", coltype=Float(), value=self.nelson_verbal_iq(), comment="Predicted WAIS verbal IQ (Nelson 1982)"), SummaryElement( name="nelson_performance_iq", coltype=Float(), value=self.nelson_performance_iq(), comment="Predicted WAIS performance IQ (Nelson 1982"), SummaryElement( name="nelson_willison_full_scale_iq", coltype=Float(), value=self.nelson_willison_full_scale_iq(), comment="Predicted WAIS-R full-scale IQ (Nelson & Willison 1991"), # noqa SummaryElement( name="bright_full_scale_iq", coltype=Float(), value=self.bright_full_scale_iq(), comment="Predicted WAIS-IV full-scale IQ (Bright 2016)"), SummaryElement( name="bright_general_ability", coltype=Float(), value=self.bright_general_ability(), comment="Predicted WAIS-IV General Ability Index (Bright 2016)"), # noqa SummaryElement( name="bright_verbal_comprehension", coltype=Float(), value=self.bright_verbal_comprehension(), comment="Predicted WAIS-IV Verbal Comprehension Index (Bright 2016)"), # noqa SummaryElement( name="bright_perceptual_reasoning", coltype=Float(), value=self.bright_perceptual_reasoning(), comment="Predicted WAIS-IV Perceptual Reasoning Index (Bright 2016)"), # noqa SummaryElement( name="bright_working_memory", coltype=Float(), value=self.bright_working_memory(), comment="Predicted WAIS-IV Working Memory Index (Bright 2016)"), # noqa SummaryElement( name="bright_perceptual_speed", coltype=Float(), value=self.bright_perceptual_speed(), comment="Predicted WAIS-IV Perceptual Speed Index (Bright 2016)"), # noqa ]
[docs] def is_complete(self) -> bool: return ( self.all_fields_not_none(WORDLIST) and self.field_contents_valid() )
def n_errors(self) -> int: e = 0 for w in WORDLIST: if getattr(self, w) is not None and not getattr(self, w): e += 1 return e
[docs] def get_task_html(self, req: CamcopsRequest) -> str: # Table rows for individual words q_a = "" 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 q_a += "<tr>" q_a += td(ACCENTED_WORDLIST[x]) q_a += td(answer(getattr(self, WORDLIST[x]))) if column == (ncolumns - 1): # last column q_a += "</tr>" row += 1 column = (column + 1) % ncolumns # Annotations nelson = "; Nelson 1982 <sup>[1]</sup>" nelson_willison = "; Nelson &amp; Willison 1991 <sup>[2]</sup>" bright = "; Bright 2016 <sup>[3]</sup>" # HTML h = """ <div class="{CssClass.SUMMARY}"> <table class="{CssClass.SUMMARY}"> {tr_is_complete} {tr_total_errors} {nelson_full_scale_iq} {nelson_verbal_iq} {nelson_performance_iq} {nelson_willison_full_scale_iq} {bright_full_scale_iq} {bright_general_ability} {bright_verbal_comprehension} {bright_perceptual_reasoning} {bright_working_memory} {bright_perceptual_speed} </table> </div> <div class="{CssClass.EXPLANATION}"> Estimates premorbid IQ by pronunciation of irregular words. </div> <table class="{CssClass.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> {q_a} </table> <div class="{CssClass.FOOTNOTES}"> [1] Nelson HE (1982), <i>National Adult Reading Test (NART): For the Assessment of Premorbid Intelligence in Patients with Dementia: Test Manual</i>, NFER-Nelson, Windsor, UK. [2] Nelson HE, Wilson J (1991) <i>National Adult Reading Test (NART)</i>, NFER-Nelson, Windsor, UK; see [3]. [3] Bright P et al (2016). The National Adult Reading Test: restandardisation against the Wechsler Adult Intelligence Scale—Fourth edition. <a href="https://www.ncbi.nlm.nih.gov/pubmed/27624393">PMID 27624393</a>. </div> <div class="{CssClass.COPYRIGHT}"> NART: Copyright © Hazel E. Nelson. Used with permission. </div> """.format( CssClass=CssClass, tr_is_complete=self.get_is_complete_tr(req), tr_total_errors=tr_qa("Total errors", self.n_errors()), nelson_full_scale_iq=tr_qa( "Predicted WAIS full-scale IQ = 127.7 – 0.826 × errors" + nelson, # noqa self.nelson_full_scale_iq() ), nelson_verbal_iq=tr_qa( "Predicted WAIS verbal IQ = 129.0 – 0.919 × errors" + nelson, self.nelson_verbal_iq() ), nelson_performance_iq=tr_qa( "Predicted WAIS performance IQ = 123.5 – 0.645 × errors" + nelson, self.nelson_performance_iq() ), nelson_willison_full_scale_iq=tr_qa( "Predicted WAIS-R full-scale IQ " "= 130.6 – 1.24 × errors" + nelson_willison, self.nelson_willison_full_scale_iq() ), bright_full_scale_iq=tr_qa( "Predicted WAIS-IV full-scale IQ " "= 126.41 – 0.9775 × errors" + bright, self.bright_full_scale_iq() ), bright_general_ability=tr_qa( "Predicted WAIS-IV General Ability Index " "= 126.5 – 0.9656 × errors" + bright, self.bright_general_ability() ), bright_verbal_comprehension=tr_qa( "Predicted WAIS-IV Verbal Comprehension Index " "= 126.81 – 1.0745 × errors" + bright, self.bright_verbal_comprehension() ), bright_perceptual_reasoning=tr_qa( "Predicted WAIS-IV Perceptual Reasoning Index " "= 120.18 – 0.6242 × errors" + bright, self.bright_perceptual_reasoning() ), bright_working_memory=tr_qa( "Predicted WAIS-IV Working Memory Index " "= 120.53 – 0.7901 × errors" + bright, self.bright_working_memory() ), bright_perceptual_speed=tr_qa( "Predicted WAIS-IV Perceptual Speed Index " "= 114.53 – 0.5285 × errors" + bright, self.bright_perceptual_speed() ), q_a=q_a, ) return h
def predict(self, intercept: float, slope: float) -> Optional[float]: if not self.is_complete(): return None return intercept + slope * self.n_errors() def nelson_full_scale_iq(self) -> Optional[float]: return self.predict(intercept=127.7, slope=-0.826) def nelson_verbal_iq(self) -> Optional[float]: return self.predict(intercept=129.0, slope=-0.919) def nelson_performance_iq(self) -> Optional[float]: return self.predict(intercept=123.5, slope=-0.645) def nelson_willison_full_scale_iq(self) -> Optional[float]: return self.predict(intercept=130.6, slope=-1.24) def bright_full_scale_iq(self) -> Optional[float]: return self.predict(intercept=126.41, slope=-0.9775) def bright_general_ability(self) -> Optional[float]: return self.predict(intercept=126.5, slope=-0.9656) def bright_verbal_comprehension(self) -> Optional[float]: return self.predict(intercept=126.81, slope=-1.0745) def bright_perceptual_reasoning(self) -> Optional[float]: return self.predict(intercept=120.18, slope=-0.6242) def bright_working_memory(self) -> Optional[float]: return self.predict(intercept=120.53, slope=-0.7901) def bright_perceptual_speed(self) -> Optional[float]: return self.predict(intercept=114.53, slope=-0.5285)
[docs] def get_snomed_codes(self, req: CamcopsRequest) -> List[SnomedExpression]: codes = [SnomedExpression(req.snomed(SnomedLookup.NART_PROCEDURE_ASSESSMENT))] # noqa if self.is_complete(): codes.append(SnomedExpression( req.snomed(SnomedLookup.NART_SCALE), { # Best value debatable: req.snomed(SnomedLookup.NART_SCORE): self.nelson_full_scale_iq(), # noqa } )) return codes