Adjuvant radiotherapy after radical prostatectomy shows no ability to improve rates of overall and cancer‐specific survival in a matched case‐control study
Bibliographic record
Abstract
OBJECTIVE: To assess the effect of adjuvant radiotherapy (aRT) on the rate of cancer-specific and overall survival after radical prostatectomy (RP) in a group of patients with a long-term follow-up, as there is controversy about the benefit of aRT after RP for prostate cancer when endpoints beyond biochemical and local recurrence are considered. PATIENTS AND METHODS: Within a study cohort of 752 patients treated with RP, 118 (15.7%) received aRT; these patients were matched with controls who did not receive aRT after RP. Exact matches were made for pT stage, RP Gleason sum, surgical margin status, age (+/-10 years), year of surgery (+/-10 years) and delivery of hormonal therapy. Kaplan-Meier and life-table analyses were used to assess overall and cancer-specific survival RESULTS: The median (range) follow-up was 11.4 (0.1-41) years. The 10- and 20-year overall survival after RP in those with no aRT were, respectively, 81.1% and 44.8%, vs 75.5% and 40.0% in the aRT group (P = 0.1). The corresponding probabilities for cause-specific survival were, respectively, 97.3% and 89.0% vs 86.3% and 69.3% (P < 0.001). There was no statistically significant difference in the overall and cause-specific survival between the groups after matching (hazard ratio 0.9, log rank P = 0.6; and 2.1, log rank P = 0.1, respectively). CONCLUSIONS: Our analysis showed that, in a matched case-control study, aRT has no effect on overall and cancer-specific survival. Further randomized long-term studies are necessary to confirm these results.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".