Predicting life expectancy in prostate cancer patients
Bibliographic record
Abstract
PURPOSE OF REVIEW: Due to its long natural history, prostate cancer illustrates best the need for tools that adequately predict life expectancy. We reviewed the actual tools available for clinicians involved in therapeutic decisions in newly diagnosed prostate cancer and examined their accuracy to provide individual life expectancy. RECENT FINDINGS: Life tables, comorbidity indices, and multivariate prognostic models can assist clinicians for life expectancy predictions. However, the accuracy of life tables (60.9%) and comorbidity indices (accuracy unknown) may be as weak as clinician-derived life expectancy predictions (69%). Actually, statistical models provide the highest accuracy (69-84.3%). To date, Walz et al. developed the most accurate model (84.3%), predicting the risk of death of nonprostate cancer-related causes within 10 years of definitive therapy. SUMMARY: Clinicians need the most accurate estimates of life expectancy in situations in which there is uncertainty regarding the need for aggressive local therapy. As the accuracy of clinician-derived life expectancy prediction is relatively modest, clinicians may benefit from assisted life expectancy prediction by life tables and statistical tools in their daily clinical practice. This would enhance the accuracy of the life expectancy predictions of individual candidates to definitive therapy for prostate cancer. Actually, nomograms provide the most accurate health-adjusted life expectancy prognostication.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".