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 predict life expectancies. In prostate cancer this prediction is especially important as it affects screening decisions. No previous studies have examined accuracy in the context of real cases and concrete end points. METHODS: : Seven clinical scenarios were summarized from charts of deceased patients. We recruited 100 medical professionals to review these scenarios and estimate each patient's life expectancy. Responses were analyzed with respect to the patients' actual survival end points, then stratified based on the demographic information provided. RESULTS: : Respondent factors, such as sex, level of training, location of work or specialty, made no significant difference on prediction accuracy. Furthermore, respondents were typically pessimistic in their estimations with a negative linear trend between estimated life expectancy and actual survival. Overall, respondents were within 1 year of actual life expectancy only 15.9% of the time; on average, respondents were 67.4% inaccurate in relation to actual survival. If framed in terms of correctly identifying which patients would live more than or less than 10 years (dichotomous accuracy), physicians were correct 68.3% of the time. CONCLUSIONS: : Physicians do poorly at predicting life expectancy and tend to underestimate how long patients have left to live. This overall inaccuracy raises the question of whether physicians should refine screening and treatment criteria, find a better proxy or 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 | 0.017 | 0.148 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| 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".