The influence of familiar non‐diagnostic information on the diagnostic decisions of novices
Bibliographic record
Abstract
CONTEXT: Previous research has demonstrated the influence of familiar symptom descriptions and entire case similarity on diagnostic reasoning. In this paper, we extend the role of familiarity to examine the influence of familiar non-diagnostic patient information (e.g. name and age) on the diagnostic decisions of novices, both immediately following training and after a delay. If an instance model (reliance on similar previously seen cases) has strong explanatory power in clinical reasoning, we should see an influence of familiar patient information on later cases containing similar identifying characteristics even though such information is objectively irrelevant. METHODS: Thirty-six participants (undergraduate psychology students) were trained to competence on four simplified psychiatric diagnoses and allowed to practise their diagnostic skills on 12 prototypical case vignettes, for which feedback was provided. One-third of participants were tested immediately, one-third following a 24-hour delay, and one-third following a 1-week delay; all were tested on novel cases. Test cases were created to have two equiprobable diagnoses, both of which were supported by two novel symptom descriptions. However, one diagnosis was also supported by non-diagnostic patient information similar to information on a patient seen in the training phase. A deviation from an equal assignment of diagnostic probability, in support of the familiar patient information, demonstrates a reliance on the familiar, non-diagnostic information, and therefore indicates an instance model of reasoning. RESULTS: Participants assigned significantly higher diagnostic probability to the diagnosis cued by the familiar patient information (52.6%) than to the plausible alternative diagnosis (38.9%). Participants also reported a higher number of clinically relevant symptoms to support the diagnosis associated with the familiar patient information than to support the plausible alternative diagnosis. The influence of familiar patient identity was consistent across delay periods and cannot be accounted for by the forgetting of diagnostic rules. CONCLUSIONS: Participants were clearly relying on familiar patient identity information as evidenced by their diagnostic conclusions and differential reporting of clinically relevant features. These results support an instance model of reasoning which is not limited by whole case similarity or similarity of diagnostic information.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.663 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".