The biasing effect of clinical history on physical examination diagnostic accuracy
Bibliographic record
Abstract
CONTEXT: Literature on diagnostic test interpretation has shown that access to clinical history can both enhance diagnostic accuracy and increase diagnostic error. Knowledge of clinical history has also been shown to enhance the more complex cognitive task of physical examination diagnosis, possibly by enabling early hypothesis generation. However, it is unclear whether clinicians adhere to these early hypotheses in the face of unexpected physical findings, thus resulting in diagnostic error. METHODS: A sample of 180 internal medicine residents received a short clinical history and conducted a cardiac physical examination on a high-fidelity simulator. Resident Doctors (Residents) were randomised to three groups based on the physical findings in the simulator. The concordant group received physical examination findings consistent with the diagnosis that was most probable based on the clinical history. Discordant groups received findings associated with plausible alternative diagnoses which either lacked expected findings (indistinct discordant) or contained unexpected findings (distinct discordant). Physical examination diagnostic accuracy and physical examination findings were analysed. RESULTS: Physical examination diagnostic accuracy varied significantly among groups (75 ± 44%, 2 ± 13% and 31 ± 47% in the concordant, indistinct discordant and distinct discordant groups, respectively (F(2,177) = 53, p < 0.0001). Of the 115 Residents who were diagnostically unsuccessful, 33% adhered to their original incorrect hypotheses. Residents verbalised an average of 12 findings (interquartile range: 10-14); 58 ± 17% were correct and the percentage of correct findings was similar in all three groups (p = 0.44). CONCLUSIONS: Residents showed substantially decreased diagnostic accuracy when faced with discordant physical findings. The majority of trainees given discordant physical findings rejected their initial hypotheses, but were still diagnostically unsuccessful. These results suggest that overcoming the bias induced by a misleading clinical history may involve two independent steps: rejection of the incorrect initial hypothesis, and selection of the correct diagnosis. Educational strategies focused solely on prompting clinicians to re-examine their hypotheses may be insufficient to reduce diagnostic error.
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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 | 0.078 | 0.369 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".