Prostate cancer detection by prostate-specific antigen-based screening in Japanese Hiroshima area shows early stage, low-grade, and low rate of cancer-specific death compared with clinical detection
Bibliographic record
Abstract
INTRODUCTION: We investigate the effectiveness of prostate-specific antigen (PSA) screening for prostate cancer. We compare the characteristics of 2 sets of patients: (1) those in whom prostate cancer was detected via PSA screening (the PS group) and (2) those in whom prostate cancer was detected at the outpatient office (the non-PS group). METHODS: Between 2002 and 2010, prostate cancer was detected in 315 patients by PSA screening. Their age, initial PSA level, pathological findings in biopsy specimens, clinical stage, and prognosis were compared with those of 497 prostate cancer patients diagnosed at the outpatient office of the Department of Urology, Hiroshima University, in the same period. RESULTS: The rates of patients with initial PSA higher than 50 ng/mL, with a Gleason score of 8 or higher, and with clinical stage D were significantly lower in the PS group than those in the non-PS group. The 5-year overall survival and cancer-specific survival in the PS group was 91.3% and 98.2%, respectively; these results were significantly better than those in the non-PS group (86.4%, p = 0.0178, and 94.9%, p = 0.0112, respectively). A Cox hazard analysis showed that PSA screening was an independent predictive factor for cancer-specific survival. CONCLUSIONS: Although our study is limited by its retrospective nature and small size, the present data indicate that prostate cancer detected in the PS group showed earlier stage, lower grade, and better prognosis than in the non-PS group.
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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 | 0.001 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".