Diabetes and Prostate Cancer Outcomes in Men with Nonmetastatic Castration-Resistant Prostate Cancer: Results from the SEARCH Cohort
Bibliographic record
Abstract
BACKGROUND: The prognosis of diabetic men with advanced prostate cancer is poorly understood and understudied. Hence, we studied associations between diabetes and progression to metastases, prostate cancer-specific mortality (PCSM) and all-cause mortality (ACM) in men with nonmetastatic castration-resistant prostate cancer (nmCRPC). METHODS: Data from men diagnosed with nmCRPC between 2000 and 2017 at 8 Veterans Affairs Health Care Centers were analyzed using Cox regression to determine HRs and 95% confidence intervals (CI) for associations between diabetes and outcomes. Men with diabetes were classified according to: (i) ICD-9/10 codes only, (ii) two HbA1c values > 6.4% (missing ICD-9/10 codes), and (iii) all diabetic men [(i) and (ii) combined]. RESULTS: Of 976 men (median age: 76 years), 304 (31%) had diabetes at nmCRPC diagnosis, of whom 51% had ICD-9/10 codes. During a median follow-up of 6.5 years, 613 men were diagnosed with metastases, and 482 PCSM and 741 ACM events occurred. In multivariable-adjusted models, ICD-9/10 code-identified diabetes was inversely associated with PCSM (HR, 0.67; 95% CI, 0.48-0.92) while diabetes identified by high HbA1c values (no ICD-9/10 codes) was associated with an increase in ACM (HR, 1.41; 95% CI, 1.16-1.72). Duration of diabetes, prior to CRPC diagnosis was inversely associated with PCSM among men identified by ICD-9/10 codes and/or HbA1c values (HR, 0.93; 95% CI, 0.88-0.98). CONCLUSIONS: In men with late-stage prostate cancer, ICD-9/10 'code-identified' diabetes is associated with better overall survival than 'undiagnosed' diabetes identified by high HbA1c values only. IMPACT: Our data suggest that better diabetes detection and management may improve survival in late-stage prostate cancer.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".