A HOSPITAL-BASED STUDY OF INITIAL OBSERVATION FOR LOW-RISK PROSTATE CANCER AND ITS PREDICTORS IN THE UNITED STATES
Bibliographic record
Abstract
INTRODUCTION: Initial observation (IO) is a strategy to minimize prostate cancer overtreatment. We sought to evaluate contemporary trends in IO utilization for low-risk prostate cancer in the United States and to identify factors associated with its uptake. METHODS: Using the National Cancer Database, we identified men with low-risk prostate cancer diagnosed between 2004 and 2011. IO utilization was plotted over time. Multivariate logistic regression was performed to determine the influence of diagnosis year and other factors on IO selection. RESULTS: Of the 219 971 men with low-risk prostate cancer, 21 231 (9.7%) underwent IO. Beginning in 2008, IO use increased significantly with time (range: 7.5%-14.3%). Compared to 2004, patients diagnosed in 2011 had 2.5 times the odds of choosing IO (odds ratio [OR] 2.5, confidence interval [CI] 2.3-2.6, p < 0.01). Aside from diagnosis year, age, race, Charlson score, clinical T stage, and PSA level predicted IO use (p < 0.01). Other predictors of IO included hospital type, insurance provider, and household income. Specifically, comprehensive cancer centres, private insurance, and higher income predicted decreased IO usage (OR 0.5, CI 0.5-0.5, p < 0.01; OR 0.4, CI 0.4-0.4, p < 0.01; and OR 0.8, CI 0.8-0.9, p < 0.01, respectively). Less educated men were also less likely to undergo observation (OR 0.8, CI 0.8-0.9, p < 0.01). Treatment within the western United States was significantly, but weakly, associated with increased use of IO (p < 0.01). CONCLUSIONS: In recent years, low-risk prostate cancer has been increasingly managed with IO, appropriately driven by patient and disease factors. Unexpectedly, observation usage also varies by race, hospital, insurance, income, and geography, suggesting that non-clinical factors may affect treatment selection.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".