Avoiding anchoring bias in unexplained chronic pain: an unexpected diagnosis of synovial osteochondromatosis
Bibliographic record
Abstract
Unconscious biases may influence clinical decision making, leading to diagnostic error. Anchoring bias occurs when a physician relies too heavily on the initial data received. We present a 57-year-old man with a 3-year history of unexplained right thigh pain who was referred to a physiatry clinic for suggestions on managing presumed non-organic pain. The patient had previously been assessed by numerous specialists and had undergone several imaging investigations, with no identifiable cause for his pain. Physical examination was challenging and there were several 'yellow flags' on history. A thorough reconsideration of the possible diagnoses led to the discovery of hip synovial osteochondromatosis as the cause for his symptoms. Over-reliance on the referral information may have led to this diagnosis being missed. In patients with unexplained pain, it is important to be aware of anchoring bias in order to avoid missing rare diagnoses.
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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 | 0.005 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".