PSA screening and prostate cancer mortality.
Bibliographic record
Abstract
BACKGROUND: Physicians have speculated that prostate-specific antigen (PSA) screening may be responsible for the reduction in prostate cancer mortality observed in the late 1 990s. In order to test this hypothesis, we assessed the relation between the change in prostate cancer incidence in the early 1990s, attributed largely to PSA screening, and the subsequent change in prostate cancer mortality. METHODS: We divided the adult male population of Quebec aged 50 years and more into 15 birth cohorts. For each birth cohort, we computed the change in prostate cancer incidence between 1989 and 1993 and the change in prostate cancer mortality between 1995 and 1999. We then assessed the correlation between the changes in prostate cancer incidence and the subsequent changes in prostate cancer mortality by weighted linear regression. We also split up the study population into 15 regional populations and repeated the analysis described above. RESULTS: We found that even though most birth cohorts showed an increase in prostate cancer incidence and a subsequent decrease in mortality, the sizes of these changes were not inversely correlated (Pearson's r = 0.33, 1-sided p = 0.89). Similarly, in the regional population study, we found that a greater increase in prostate cancer incidence did not indicate a greater decline in mortality (Pearson's r= 0.13, 1-sided p = 0.68). INTERPRETATION: These results suggest that for our study population PSA screening was not associated with, and therefore cannot explain, the decline in prostate cancer mortality.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".