Initial diagnostic hypotheses bias analytic information processing in non‐visual domains
Bibliographic record
Abstract
CONTEXT: Previous studies have shown that an initial diagnostic hypothesis biases automatic information processing. It is unclear if an initial hypothesis has a similar effect on analytic information processing. Our first objective was to study the effect of an initial diagnostic hypothesis on analytic processing. Our second objective was to assess the effect of clinical experience on analytic processing by evaluating the effect of clinical frequency and urgency of an alternative diagnosis on diagnosis selection. METHODS: During a 12-minute objective structured clinical examination station, 19 subspecialty medical residents diagnosed the cause of 3 clinical presentations: dyspnoea; headache, and chest pain. Subjects were randomly allocated cases for which the suggested initial hypothesis was either correct or incorrect. For cases with an incorrect initial hypothesis, the alternative diagnoses varied in the frequency with which they are encountered in clinical practice, and their clinical urgency, relative to the initial diagnostic hypothesis. RESULTS: All correct initial hypotheses were retained, compared with 10.9% of incorrect hypotheses. All cases with a correct initial hypothesis were diagnosed correctly, compared with 65.2% of cases with an incorrect hypothesis (risk ratio 1.5 [95% confidence interval 1.2-1.9], P = 0.02). Clinical frequency and urgency were not associated with alternative diagnosis selection. DISCUSSION: Our results suggest that an initial diagnostic hypothesis biases analytic processing. The data used to reject an initial hypothesis appear to drive selection of an alternative hypothesis. Further studies aimed at finding strategies for increasing the likelihood of generating a correct initial hypothesis or debiasing an initial hypothesis are needed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.018 | 0.231 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".