Clinicians are poor raters of life‐expectancy before radical prostatectomy or definitive radiotherapy for localized prostate cancer
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it