Identifying Oncology Patients at High-risk for Potentially Preventable Emergency Department Visits (PPEDs) at a Single Institution in Toronto, Canada
Bibliographic record
Abstract
Background: Reducing potentially preventable emergency department visits (PPEDs) is important. This study aims to define and describe oncology-related PPEDs at a single institution and use machine learning (ML) to help identify oncology patients at highest risk for PPEDs. Methods: A retrospective cohort study was conducted among five databases. ED visits by oncology patients between April 1st, 2019 – April 1st, 2021 from a single institution were collected. Trends in PPEDs were evaluated using descriptive statistics, logistic regression, and ML modelling.Results: 6,689 oncology patients visited the ED (n=13,415 visits) during the study period. 62.1% were classified as PPEDs. High-risk groups for PPEDs included stage 1-3 breast cancer patients and adjuvant systemic therapy patients. The highest-performing ML model scored an AUC = 0.819. Conclusions: High-risk groups for PPEDs include stage 1-3 breast cancer patients undergoing systemic therapy. Our novel definition of PPEDs at this stage appears reasonable. Future research to validate this work can be impactful.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".