Validation of a classification to identify emergency department visits suitable for subacute and virtual care models: a randomised single-blinded agreement study protocol
Bibliographic record
Abstract
INTRODUCTION: Redirecting suitable patients from the emergency department (ED) to alternative subacute settings may assist in reducing ED overcrowding while delivering equivalent care. The Emergency Department Avoidance Classification (EDAC) was constructed to retrospectively classify ED visits that may have been suitable for safe management in a subacute or virtual clinical setting. The EDAC has established face and content validity but has not been tested against a reference standard as a criterion. OBJECTIVES: Our primary objective is to examine the agreement between the EDAC and ED physician judgements in retrospectively identifying ED visits suitable for subacute care management. Our secondary objective is to assess the validity of ED physicians' judgement as a criterion standard. Our tertiary objective is to examine how the ED physician's perception of a virtual ED care alternative correlates with the EDAC. METHODS AND ANALYSIS: A randomised single-centre, single-blinded agreement study. We will randomly select ED charts between 1 January and 31 December 2019 from an academic hospital in Hamilton, Canada. ED charts will be randomly assigned to participating ED physicians who will evaluate if this ED visit could have been managed appropriately and safely in a subacute and/or virtual model of care. Each chart will be reviewed by two physicians independently. We compute our needed sample size to be 79 charts. We will use kappa statistics to measure inter-rater agreement. A repeated measures regression model of physician ratings will provide variance estimates that we will use to assess the intraclass correlation of ED physician ratings and the EDAC. ETHICS AND DISSEMINATION: This study has been approved by the Hamilton Integrated Research Ethics Board (2022-14625). If validated, the EDAC may provide an ED-based classification to identify potentially avoidable ED visits, monitor ED visit trends, and proactively delineate those best suited for subacute or virtual care models.
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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 | 0.188 | 0.198 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.017 | 0.005 |
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".