Clinicians are poor raters of life‐expectancy before radical prostatectomy or definitive radiotherapy for localized prostate cancer
Bibliographic record
Abstract
OBJECTIVE: To test the accuracy of predicting life-expectancy (LE) among 19 raters, as the accurate prediction of LE in candidates for definitive therapy for localized prostate cancer is crucial, and little is known of the ability of clinicians to predict LE. SUBJECTS AND METHODS: We randomly selected the case-vignettes of 50 patients treated with either radical prostatectomy (RP, 25) or external beam radiotherapy (EBRT, 25) for prostate cancer, and who either survived for > 10 years or died earlier with no evidence of disease relapse. The median age at treatment was 67 years and the median Charlson Comorbidity Index (CCI) was 2. The raters consisted of urology staff (six), urology residents (10) and medical students (three). The case-vignettes included patient age, comorbidities and CCI score, and raters were asked to predict the survival at 10 years (yes vs no), assuming no disease relapse. RESULTS: Of the 50 cases, 20 (40%) did not survive for > 10 years; clinicians estimated a mean (range) of 23 (10-35) deaths before 10 years. The mean (95% confidence interval) overall predictive accuracy (0.5 = chance, 1.0 = perfect prediction) of LE predictions was 0.68 (0.64-0.71). Individual accuracy ranged from 0.52 (staff) to 0.78 (staff). There were no important differences among the rater groups (residents 0.69 vs staff 0.67 vs medical students 0.67). CONCLUSIONS: Clinicians are relatively poor at predicting LE; tools to predict LE might be able to improve clinicians' performance in this important part of decision-making about prostate cancer treatment. It remains to be determined whether this limitation exclusively applies to prostate cancer or also to other malignancies.
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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 | 0.010 | 0.105 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".