Personalized prostate cancer screening accounting for individual risk factors and preferences
Bibliographic record
Abstract
Background The benefit-harm balance of prostate cancer (PCa) screening is influenced by individual risk factors, preferences, and specifics of the applied screening algorithm. We used the ONCOTYROL Prostate Cancer Outcome and Policy (PCOP) model to identify optimal screening strategies with respect to individual family history, age, disutility weighting, and life-shortening co-morbidity. Methods The PCOP model is a state-transition micro-simulation model simulating the consequences of PCa screening and treatment on duration and quality of life. Evaluated strategies included no screening, one-time screening at different ages, and interval screening at different intervals and age ranges followed by immediate treatment. Sensitivity analyses were used to identify strategies maximizing quality-adjusted life expectancy (QALE) for each combination of individual risk factors and disutility weighting. Screening was also evaluated in combination with biennial active surveillance (AS) delaying treatment of localized cancer until progression to Gleason score ≥ 7. Results In men without elevated familial PCa risk, no screening was the preferred strategy, independent of age, disutility weighting and life-shortening co-morbidity. In contrast, men with elevated familial risk gained QALE depending on their risk and preference constellation. Optimal screening strategies varied as well. AS improved the benefit-harm balance of some screening strategies. However, strategies gaining the most QALE in men with familial risk gained less when combined with AS. Conclusions Based on our model assumptions, PCa screening is beneficial for men with familial predisposition only. However, benefits of screening depend on individual risks and preferences. AS may reduce benefits of screening, when gains by averted overtreatment are outweighed by losses due to delayed treatment. Key messages: PCa screening may be beneficial for men with elevated familial PCa risk, but not for men with average PCa risk Decisions on PCa screening should incorporate individual risks and preferences
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".