The information distortion bias: implications for medical decisions
Bibliographic record
Abstract
CONTEXT: Every diagnosis involves an act of decision making, which requires proper evaluation of information. However, even seemingly objective information can require interpretation, often without our conscious awareness. In this cross-cutting edge article we describe the phenomenon of leader-driven information distortion (ID) and its implications for medical education. INFORMATION DISTORTION: Recent research indicates that one threat to good decisions is a biased interpretation of information to favour one alternative course of action over another. Once an alternative emerges as a leader during a decision there is a strong tendency to evaluate subsequent information as supporting that option. This can occur when deciding between two competing diagnoses. It is particularly a concern if diagnostic tests provide potentially ambiguous results. This leader-driven ID is pre-decisional in nature, in that it develops during a decision and involves the interpretation of information available prior to the final decision or diagnosis, with different interpretations possible depending on whichever alternative is the leader. Studies reveal that the distortion bias is pervasive in decisions, and that awareness of the act of distortion is low in decision makers. APPLICATION TO MEDICAL EDUCATION: Empirical research has confirmed the presence of leader-driven ID in hypothetical diagnoses made by physicians. ID creates two threats to medical decisions: First, it can make a diagnosis sticky in that it is resistant to being overturned by contradictory information. Second, it can promote unwarranted certainty in a diagnosis. The outcome may be premature closure, unnecessary testing or incorrect treatment, resulting in delayed or missed diagnoses. METHODS: This paper summarises research related to leader-driven ID in medical and professional decisions and discusses various approaches directed towards reducing ID. A framework and language are provided for thinking about and discussing ID in medical decisions and medical education. Courses of action for mitigating the effects of ID are suggested.
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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 | 0.136 | 0.450 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.028 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".