Prevalence of incidental prostate cancer: A systematic review of autopsy studies
Bibliographic record
Abstract
Prostate cancer screening may detect nonprogressive cancers, leading to overdiagnosis and overtreatment. The potential for overdiagnosis can be assessed from the reservoir of prostate cancer in autopsy studies that report incidental prostate cancer rates in men who died of other causes. We aimed to estimate the age-specific incidental cancer prevalence from all published autopsy studies. We identified eligible studies by searches of Medline and Embase, forward and backward citation searches and contacting authors. We screened the titles and abstracts of all articles; checked the full-text articles for eligibility and extracted clinical and pathology data using standardized forms. We extracted mean cancer prevalence, age-specific cancer prevalence and validity measures and then pooled data from all studies using logistic regression models with random effects. The 29 studies included in the review dated from 1948 to 2013. Incidental cancer was detected in all populations, with no obvious time trends in prevalence. Prostate cancer prevalence increased with each decade of age, OR = 1.7 (1.6-1.8), and was higher in studies that used the Gleason score, OR = 2.0 (1.1-3.7). No other factors were significantly predictive. The estimated mean cancer prevalence increased in a nonlinear fashion from 5% (95% CI: 3-8%) at age <30 years to 59% (95% CI: 48-71%) by age >79 years. There was substantial variation between populations in estimated cancer prevalence. There is a substantial reservoir of incidental prostate cancer which increases with age. The high risk of overdiagnosis limits the usefulness of prostate cancer screening.
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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 | 0.012 | 0.059 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.016 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".