Cohort profile: A multicenter evaluation of clinical decision rules applied to emergency department triage of patients presenting with acute respiratory infection or infectious diarrhea
Bibliographic record
Abstract
ABSTRACT Purpose Emergency department (ED) patients suffering from acute respiratory infection or infectious diarrhea often present with self-limiting conditions. The study objective was to evaluate the performance of triage clinical decision rules consisting of a rapid molecular test and a self-administered patient questionnaire to identify ED patients who can self-treat at home without consulting an emergency physician. This article describes the profile of the cohorts recruited. Participants Participants were prospectively recruited in 4 EDs in Québec City and Montréal, Canada, from February 2022 through March 2023. Participants were aged ≥18 years, had an acute respiratory infection and/or acute infectious diarrhea, and had received a Canadian Triage and Acuity Scale score between 3 (urgent) and 5 (non-urgent). Participants were asked to complete a self-administered risk stratification questionnaire after triage and to follow usual ED care afterward. Nasopharyngeal and/or rectal swabs were collected and frozen for subsequent testing on a rapid molecular testing device. Data were obtained during the recruitment visit, during a follow-up phone call 7 days later and from medical records. The primary outcome to be predicted by the clinical decision rules was an aggregation of hospitalization, return visit and mortality at 7 days. Findings to date We recruited 1,391 participants, 62.3% of whom were women, 80.7% were aged under 60, 78.2% had no comorbidities, 76.5% presented with an acute respiratory infection, 17.8% with an acute infectious diarrhea and 5.7% with both. Hospitalization and return visits incidence proportions at 7 days were respectively 10.8% and 13.1% for respiratory infections and 14.1% and 16.5% for infectious diarrhea. No death was recorded. Future plans The data gathered from these cohorts will enable us to test, refine, derive, and validate clinical decision rules used to help ED triage nurses offer the most suitable care to patients presenting with acute respiratory infections or infectious diarrhea. Strengths and limitations Our study has both strengths and limitations. Among the strengths: The cohorts were recruited from 4 different EDs and reached the target sample size for acute respiratory infections and acute infectious diarrhea. The potential economic impact of the clinical decision rules will be assessed from the perspective of both the health system and the patient. The main limitations are the following. Cohorts were recruited by convenience sampling and may not be representative of the entire ED population. The patient self-administered questionnaires used in this study were derived from systematic reviews and rapid prototyping, but not according to the methodological standards recommended for the derivation of clinical decision rules. However, the study dataset was built to enable rules to be refined and if necessary, new rules to be derived and internally validated. We recorded a 12.9% loss of participants at the 7-day follow-up phone call. However, the primary outcome measures (return visits, admissions and deaths) will be obtained from provincial administrative databases. These reliable data will enable us to overcome this limitation for future projects to refine and validate robust triage clinical decision rules.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.021 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".