Accuracy of life tables in predicting overall survival in patients after radical prostatectomy
Bibliographic record
Abstract
OBJECTIVE: To test the accuracy of life tables (LT), the standard tool for predicting life-expectancy (LE), but the accuracy of which is unknown in patients with prostate cancer, where the 10-year LE is a widely accepted threshold for the delivery of definitive therapy. PATIENTS AND METHODS: We tested the accuracy of predictions of LE from LT in 9678 men treated with radical prostatectomy (RP) for prostate cancer. The predictions of LE from LT at 10 years after RP were compared to Kaplan Meier-derived 10-year survival values. Moreover, the accuracy of LT predictions was quantified in a Cox-regression using Harrell's concordance index. To control for the effect of prostate cancer mortality, analyses were repeated in a subset of 5955 patients with no evidence of disease recurrence. Additional stratification schemes were applied to control for age and comorbidity. RESULTS: At RP, the median age was 64 years, the median Charlson Comorbidity Index (CCI) was 1 and the median LT-derived LE was 16 years. The median actuarial survival was not reached (mean 12.4 years). In the whole group the LT-predicted 10-year survival was 96.8%, vs an observed of 75.3%. In men with no disease recurrence the LT-predicted survival was 97.3%, vs 81.1% observed. After age and CCI stratification, LT overestimated the 10-year survival the most in those aged 65-69 years and in patients with CCI scores of >2. CONCLUSION: The overestimation of LE can lead to overtreatment of prostate cancer, especially in those men who die early from other causes.
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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 | 0.004 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".