Challenging the 10-year rule: The accuracy of patient life expectancy predictions by physicians in relation to prostate cancer management
Bibliographic record
Abstract
Introduction: We assess physicians’ ability to accurately predictlife expectancies. In prostate cancer this prediction is especiallyimportant as it affects screening decisions. No previous studieshave examined accuracy in the context of real cases and concreteend points.Methods: Seven clinical scenarios were summarized from chartsof deceased patients. We recruited 100 medical professionals toreview these scenarios and estimate each patient’s life expectancy.Responses were analyzed with respect to the patients’ actual survivalend points, then stratified based on the demographic informationprovided.Results: Respondent factors, such as sex, level of training, locationof work or specialty, made no significant difference on predictionaccuracy. Furthermore, respondents were typically pessimistic intheir estimations with a negative linear trend between estimated lifeexpectancy and actual survival. Overall, respondents were within 1year of actual life expectancy only 15.9% of the time; on average,respondents were 67.4% inaccurate in relation to actual survival. Ifframed in terms of correctly identifying which patients would livemore than or less than 10 years (dichotomous accuracy), physicianswere correct 68.3% of the time.Conclusions: Physicians do poorly at predicting life expectancyand tend to underestimate how long patients have left to live.This overall inaccuracy raises the question of whether physiciansshould refine screening and treatment criteria, find a better proxyor dispose of the criteria altogether.
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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 | 0.021 | 0.161 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".