Source code for camcops_server.tasks.factg

#!/usr/bin/env python
# camcops_server/tasks/factg.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/>.

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

- By Joe Kearney, Rudolf Cardinal.

"""

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

from cardinal_pythonlib.stringfunc import strseq
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_db import add_multiple_columns
from camcops_server.cc_modules.cc_html import (
    answer,
    tr_qa,
    subheading_spanning_two_columns,
    tr
)
from camcops_server.cc_modules.cc_request import CamcopsRequest
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 (
    get_from_dict,
    Task,
    TaskHasPatientMixin,
)
from camcops_server.cc_modules.cc_text import SS
from camcops_server.cc_modules.cc_trackerhelpers import (
    TrackerAxisTick,
    TrackerInfo,
)


# =============================================================================
# Fact-G
# =============================================================================

DISPLAY_DP = 2
MAX_QSCORE = 4
NON_REVERSE_SCORED_EMOTIONAL_QNUM = 2


class FactgMetaclass(DeclarativeMeta):
    # noinspection PyInitNewSignature
    def __init__(cls: Type['Factg'],
                 name: str,
                 bases: Tuple[Type, ...],
                 classdict: Dict[str, Any]) -> None:
        answer_stem = (
            " (0 not at all, 1 a little bit, 2 somewhat, 3 quite a bit, "
            "4 very much)"
        )
        add_multiple_columns(
            cls, "p_q", 1, cls.N_QUESTIONS_PHYSICAL,
            minimum=0, maximum=4,
            comment_fmt="Physical well-being Q{n} ({s})" + answer_stem,
            comment_strings=[
                "lack of energy",
                "nausea",
                "trouble meeting family needs",
                "pain",
                "treatment side effects",
                "feel ill",
                "bedbound",
            ],
        )
        add_multiple_columns(
            cls, "s_q", 1, cls.N_QUESTIONS_SOCIAL,
            minimum=0, maximum=4,
            comment_fmt="Social well-being Q{n} ({s})" + answer_stem,
            comment_strings=[
                "close to friends",
                "emotional support from family",
                "support from friends",
                "family accepted illness",
                "good family comms re illness",
                "feel close to partner/main supporter",
                "satisfied with sex life",
            ],
        )
        add_multiple_columns(
            cls, "e_q", 1, cls.N_QUESTIONS_EMOTIONAL,
            minimum=0, maximum=4,
            comment_fmt="Emotional well-being Q{n} ({s})" + answer_stem,
            comment_strings=[
                "sad",
                "satisfied with coping re illness",
                "losing hope in fight against illness",
                "nervous"
                "worried about dying",
                "worried condition will worsen",
            ],
        )
        add_multiple_columns(
            cls, "f_q", 1, cls.N_QUESTIONS_FUNCTIONAL,
            minimum=0, maximum=4,
            comment_fmt="Functional well-being Q{n} ({s})" + answer_stem,
            comment_strings=[
                "able to work",
                "work fulfilling",
                "able to enjoy life",
                "accepted illness",
                "sleeping well",
                "enjoying usual fun things",
                "content with quality of life",
            ],
        )
        super().__init__(name, bases, classdict)


[docs]class FactgGroupInfo(object): """ Internal information class for the FACT-G. """ def __init__(self, heading_xstring_name: str, question_prefix: str, fieldnames: List[str], summary_fieldname: str, summary_description: str, max_score: int, reverse_score_all: bool = False, reverse_score_all_but_q2: bool = False) -> None: self.heading_xstring_name = heading_xstring_name self.question_prefix = question_prefix self.fieldnames = fieldnames self.summary_fieldname = summary_fieldname self.summary_description = summary_description self.max_score = max_score self.reverse_score_all = reverse_score_all self.reverse_score_all_but_q2 = reverse_score_all_but_q2 self.n_questions = len(fieldnames) def subscore(self, task: "Factg") -> float: answered = 0 scoresum = 0 for qnum, fieldname in enumerate(self.fieldnames, start=1): answer_val = getattr(task, fieldname) try: answer_int = int(answer_val) except (TypeError, ValueError): continue answered += 1 if (self.reverse_score_all or (self.reverse_score_all_but_q2 and qnum != NON_REVERSE_SCORED_EMOTIONAL_QNUM)): # reverse-scored scoresum += MAX_QSCORE - answer_int else: # normally scored scoresum += answer_int if answered == 0: return 0 return scoresum * self.n_questions / answered
[docs]class Factg(TaskHasPatientMixin, Task, metaclass=FactgMetaclass): """ Server implementation of the Fact-G task. """ __tablename__ = "factg" shortname = "FACT-G" provides_trackers = True N_QUESTIONS_PHYSICAL = 7 N_QUESTIONS_SOCIAL = 7 N_QUESTIONS_EMOTIONAL = 6 N_QUESTIONS_FUNCTIONAL = 7 MAX_SCORE_PHYSICAL = 28 MAX_SCORE_SOCIAL = 28 MAX_SCORE_EMOTIONAL = 24 MAX_SCORE_FUNCTIONAL = 28 N_ALL = ( N_QUESTIONS_PHYSICAL + N_QUESTIONS_SOCIAL + N_QUESTIONS_EMOTIONAL + N_QUESTIONS_FUNCTIONAL ) MAX_SCORE_TOTAL = N_ALL * MAX_QSCORE PHYSICAL_PREFIX = "p_q" SOCIAL_PREFIX = "s_q" EMOTIONAL_PREFIX = "e_q" FUNCTIONAL_PREFIX = "f_q" QUESTIONS_PHYSICAL = strseq(PHYSICAL_PREFIX, 1, N_QUESTIONS_PHYSICAL) QUESTIONS_SOCIAL = strseq(SOCIAL_PREFIX, 1, N_QUESTIONS_SOCIAL) QUESTIONS_EMOTIONAL = strseq(EMOTIONAL_PREFIX, 1, N_QUESTIONS_EMOTIONAL) QUESTIONS_FUNCTIONAL = strseq(FUNCTIONAL_PREFIX, 1, N_QUESTIONS_FUNCTIONAL) GROUPS = [ FactgGroupInfo("h1", PHYSICAL_PREFIX, QUESTIONS_PHYSICAL, "physical_wellbeing", "Physical wellbeing subscore", MAX_SCORE_PHYSICAL, reverse_score_all=True), FactgGroupInfo("h2", SOCIAL_PREFIX, QUESTIONS_SOCIAL, "social_family_wellbeing", "Social/family wellbeing subscore", MAX_SCORE_SOCIAL), FactgGroupInfo("h3", EMOTIONAL_PREFIX, QUESTIONS_EMOTIONAL, "emotional_wellbeing", "Emotional wellbeing subscore", MAX_SCORE_EMOTIONAL, reverse_score_all_but_q2=True), FactgGroupInfo("h4", FUNCTIONAL_PREFIX, QUESTIONS_FUNCTIONAL, "functional_wellbeing", "Functional wellbeing subscore", MAX_SCORE_FUNCTIONAL), ] OPTIONAL_Q = "s_q7" ignore_s_q7 = CamcopsColumn("ignore_s_q7", Boolean, permitted_value_checker=BIT_CHECKER)
[docs] @staticmethod def longname(req: "CamcopsRequest") -> str: _ = req.gettext return _("Functional Assessment of Cancer Therapy — General")
[docs] def is_complete(self) -> bool: questions_social = self.QUESTIONS_SOCIAL.copy() if self.ignore_s_q7: questions_social.remove(self.OPTIONAL_Q) all_qs = [self.QUESTIONS_PHYSICAL, questions_social, self.QUESTIONS_EMOTIONAL, self.QUESTIONS_FUNCTIONAL] for qlist in all_qs: if self.any_fields_none(qlist): return False if not self.field_contents_valid(): return False return True
[docs] def get_trackers(self, req: CamcopsRequest) -> List[TrackerInfo]: return [TrackerInfo( value=self.total_score(), plot_label="FACT-G total score (rating well-being)", axis_label=f"Total score (out of {self.MAX_SCORE_TOTAL})", axis_min=-0.5, axis_max=self.MAX_SCORE_TOTAL + 0.5, axis_ticks=[ TrackerAxisTick(108, "108"), TrackerAxisTick(100, "100"), TrackerAxisTick(80, "80"), TrackerAxisTick(60, "60"), TrackerAxisTick(40, "40"), TrackerAxisTick(20, "20"), TrackerAxisTick(0, "0"), ], horizontal_lines=[ 80, 60, 40, 20 ], )]
[docs] def get_summaries(self, req: CamcopsRequest) -> List[SummaryElement]: elements = self.standard_task_summary_fields() for info in self.GROUPS: subscore = info.subscore(self) elements.append(SummaryElement( name=info.summary_fieldname, coltype=Float(), value=subscore, comment=f"{info.summary_description} (out of {info.max_score})" )) elements.append(SummaryElement( name="total_score", coltype=Float(), value=self.total_score(), comment=f"Total score (out of {self.MAX_SCORE_TOTAL})" )) return elements
def subscores(self) -> List[float]: sscores = [] for info in self.GROUPS: sscores.append(info.subscore(self)) return sscores def total_score(self) -> float: return sum(self.subscores())
[docs] def get_task_html(self, req: CamcopsRequest) -> str: answers = { None: None, 0: "0 — " + self.wxstring(req, "a0"), 1: "1 — " + self.wxstring(req, "a1"), 2: "2 — " + self.wxstring(req, "a2"), 3: "3 — " + self.wxstring(req, "a3"), 4: "4 — " + self.wxstring(req, "a4"), } subscore_html = "" answer_html = "" for info in self.GROUPS: heading = self.wxstring(req, info.heading_xstring_name) subscore = info.subscore(self) subscore_html += tr( heading, ( answer(round(subscore, DISPLAY_DP)) + f" / {info.max_score}" ) ) answer_html += subheading_spanning_two_columns(heading) for q in info.fieldnames: if q == self.OPTIONAL_Q: # insert additional row answer_html += tr_qa( self.xstring(req, "prefer_no_answer"), self.ignore_s_q7) answer_val = getattr(self, q) answer_html += tr_qa(self.wxstring(req, q), get_from_dict(answers, answer_val)) tscore = round(self.total_score(), DISPLAY_DP) tr_total_score = tr( req.sstring(SS.TOTAL_SCORE), answer(tscore) + f" / {self.MAX_SCORE_TOTAL}" ) return f""" <div class="{CssClass.SUMMARY}"> <table class="{CssClass.SUMMARY}"> {self.get_is_complete_tr(req)} {tr_total_score} {subscore_html} </table> </div> <table class="{CssClass.TASKDETAIL}"> <tr> <th width="50%">Question</th> <th width="50%">Answer</th> </tr> {answer_html} </table> """