Localized prostate cancer: An analysis of the CDC Breast and Prostate Cancer Data Quality and Patterns of Care study (CDC PoC-BP)
Bibliographic record
Abstract
INTRODUCTION: Limited evidence exists on the comparative effectiveness of local treatments for prostate cancer (PCa) due to the lack of generalizability. Using granular national data, we sought to examine the association between radical prostatectomy (RP) and intensity-modulated radiation therapy (IMRT) treatment and survival. METHODS: Records were abstracted for localized PCa cases diagnosed in 2004 across seven state registries to identify patients undergoing RP (n=3019) or IMRT (n=667). Comorbidity was assessed by the Adult Comorbidity Evaluation-27 (ACE-27). Propensity score matching (PSM) was used to balance covariates between treatment groups. All-cause and PCa-specific mortality were primary endpoints. A subgroup analysis of patients with high-risk PCa (RP, n=89; IMRT, n=95) was conducted. RESULTS: Following PSM, matched patients (n=502 pairs) treated with either RP or IMRT were well-balanced with respect to covariates. With a median followup of 10.5 years (interquartile range [IQR] 9.9-11.0), the 11-year overall survival (OS) was 71.2% (95% confidence interval [CI] 66.9-75.8) for RP and 62.3% (95% CI 57.4-67.6) for IMRT. IMRT was associated with a 41% increased risk of all-cause mortality (hazard ratio [HR] 1.41, 95% CI 1.13-1.76) but not PCa-specific mortality (HR 1.75, 95% CI 0.84-3.64), as compared to RP. In patients with high-risk PCa, IMRT, as compared to RP, was not associated with a statistically significant difference in all-cause (HR 1.53, 95% CI 0.97-2.42) or PCa-specific mortality (HR 1.92, 95% CI 0.69-5.36). CONCLUSIONS: Despite a low mortality rate at 10 years and possible residual confounding, we found a significantly increased risk of all-cause mortality but no PCa-specific mortality associated with IMRT as compared to RP in this population-based study.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".