The Role of Differential Diagnoses in Self‐Triage Decision‐Making
Bibliographic record
Abstract
Self‐triage, or the decision if and when to seek medical care is crucial, but also intrinsically difficult. The current study evaluates how the presence of competing diagnoses with differing severities influences participants' likelihood of seeking care. Participants were healthy undergraduate students from McMaster University. In a within‐subjects design, participants rated the urgency with which they would seek medical care for a series of hypothetical scenarios. Each scenario included symptoms and either a low‐severity diagnosis, a high‐severity diagnosis, or a differential diagnosis where both high‐ and low‐severity options were presented. Participants rated low‐severity diagnoses as less urgent than high‐severity diagnoses, as expected. Critically, when presented with both low‐ and high‐severity options, participants rated scenarios with an intermediate level of urgency. Further analyses showed that participants appeared to base their urgency judgments on the low‐severity diagnosis and then adjust their ratings upward when presented with a high‐severity alternative. The results demonstrate that even when one of the possible diagnoses presented would require immediate care if accurate, ratings of urgency were significantly decreased if another less serious alternative was also suggested, potentially leading to sub‐optimal decision‐making. Implications of this observed pattern of self‐triage decision‐making are discussed.
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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 | 0.008 | 0.066 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".