USPSTF Recommendation Against PSA Screening vs. Stage and Cancer‐Specific Mortality in Localized Prostate Cancer
Bibliographic record
Abstract
BACKGROUND: The USPSTF recommendation against PSA screening (RAPS) in 2012 resulted in unfavorable changes in prostate cancer (PCa) outcomes. However, the effect on cancer-specific mortality (CSM) in localized PCa has not been assessed. METHODS: Within the Surveillance, Epidemiology, and End Results database (2004-2021), we identified patients treated with radiotherapy (RT) or radical prostatectomy (RP) for localized PCa. Time trends were examined using least-squares linear regression. Multivariable Cox regression was used to study the association between RAPS and PCa-mortality. RESULTS: Of 270,092 patients aged < 75 years, 191,621 (70.1%) were treated before and 78,471 (29.1%) in the RAPS era. CSM at 6 years of follow-up was 1.6% (95% confidence interval [CI]: 1.6, 1.7) before and 1.9% (95%CI: 1.8, 2.0) in the RAPS era (p < 0.001). In multivariable Cox models adjusted to patient characteristics, RAPS era independently predicted 1.2-fold higher CSM overall (95%CI: 1.1, 1.3; p < 0.001), 1.3-fold higher CSM in RP-patients (95%CI: 1.1, 1.4; p < 0.001), and 1.1-fold higher CSM in RT-patients (95%CI: 1.02, 1.2; p = 0.02) aged < 75 years. Of 33,688 patients aged ≥ 75 years, 12,485 (37.1%) were treated before and 21,203 (62.9%) in the RAPS era. CSM at 6 years of follow-up was 4.2% (95%CI: 3.8, 4.6) before and 4.8% (95%CI: 4.5, 5.1) in the RAPS era (p = 0.002). In multivariable Cox models adjusted to patient characteristics, RAPS era did not predict higher CSM overall, in RP-patients, or in RT-patients (all p ≥ 0.5) aged ≥ 75 years. Limitations include changes in early detection and disease management over time, which might have impacted CSM as well. CONCLUSIONS: The USPSTF RAPS introduction resulted in a 1.2-fold higher CSM in localized PCa patients aged < 75 years, but not in patients aged ≥ 75 years. The time trend analysis suggested that this negative effect has become increasingly pronounced since the USPSTF RAPS.
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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 | 0.042 | 0.131 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.007 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".