The Beguiling Pursuit of More Information
Bibliographic record
Abstract
BACKGROUND: The authors tested whether clinicians make different decisions if they pursue information than if they receive the same information from the start. METHODS: Three groups of clinicians participated (N=1206): dialysis nurses (n=171), practicing urologists (n=461), and academic physicians (n=574). Surveys were sent to each group containing medical scenarios formulated in 1 of 2 versions. The simple version of each scenario presented a choice between 2 options. The search version presented the same choice but only after some information had been missing and subsequently obtained. The 2 versions otherwise contained identical data and were randomly assigned. RESULTS: In one scenario involving a personal choice about kidney donation, more dialysis nurses were willing to donate when they first decided to be tested for compatibility and were found suitable than when theyknew they were suitable from the start (65% vs. 44%, P= 0.007). Similar discrepancies were found in decisions made by practicing urologists concerning surgery for a patient with prostate cancer and in decisions of academic physicians considering emergency management for a patient with acute chest pain. CONCLUSIONS: The pursuit of information can increase its salience and cause clinicians to assign more importance to the information than if the same information was immediately available. An awareness of this cognitive bias may lead to improved decision making in difficult medical situations.
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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 | 0.032 | 0.219 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".