Delay in surgical therapy for clinically localized prostate cancer and biochemical recurrence after radical prostatectomy.
Bibliographic record
Abstract
BACKGROUND: In Canada, waiting times for cancer care have been increasing, particularly for patients with genitourinary malignancies. We examined whether delay from diagnosis for patients undergoing surgery for clinically localized prostate cancer affects cancer cure rates. METHODS: We conducted a historical cohort study among 645 patients who underwent radical prostatectomy between 1987 and 1997, using biochemical recurrence (PSA elevation) and metastasis as endpoints. We examined whether patients who underwent surgery >/= months (delayed surgery group) from the date of diagnosis had reduced recurrence-free survival, compared to patients who had surgery <3 months (early surgery group) from the date of diagnosis, adjusting for grade, stage and PSA level at diagnosis. RESULTS: The crude 10-year recurrence-free and metastasis-free survival rates for all patients were 71.1% (95% C.I.: 64.9% - 77.3%) and 95.3% (95% C.I.: 91.3% - 99.3%), respectively. Of the 645 patients, 189 (29.3%) had surgery >/= months after diagnosis. The median time from the date of diagnosis to surgery was 68 days (range 15 to 951 days). The 10-year recurrence-free survival was higher for patients who underwent early surgery (74.6%, 95% C.I.: 67.9% - 81.4%) compared to patients in the delayed surgery group (61.3%, 95% C.I.: 46.7% - 76.0%, p=0.05). The crude and adjusted hazard ratios for developing biochemical recurrence for patients in the delayed surgery group were 1.58 (95% C.I.: 1.0 - 2.4, p=0.04) and 1.46 (95% C.I.: 0.9 - 2.3, p=0.09), respectively, compared to patients who underwent early surgery. CONCLUSIONS: There may exist a possible relationship between delays from diagnosis for radical prostatectomy and prostate cancer cure rates. These findings may have many biases that could not be properly accounted in this retrospective analysis and larger cohort analyses will be required to confirm these findings.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".