American trends in expectant management utilization for prostate cancer from 2000 to 2009
Bibliographic record
Abstract
INTRODUCTON: The overtreatment of early prostate cancer has become a major public health concern. Expectant management (EM) is a strategy to minimize overtreatment, but little is known about its pattern of use. We sought to examine national EM utilization over the preceding decade. METHODS: We examined prostate cancer treatment utilization from 2000 to 2009 using the National Cancer Database. EM use was analyzed in relation to other treatments and by cancer stage, age group, Charlson score, and hospital practice setting. RESULTS: Overall, 109 997 (8.2%) men were managed initially with EM. EM usage remained stable at 7.6% to 9.5% from 2000 to 2009 with no appreciable increase for low-stage cancers. Usage was only slightly higher in elderly patients and in patients with multiple comorbidities. Veterans Affairs and low-volume hospitals had a much higher and increasing EM rate (range: 18.8%-29.8% and 15.1%-24.2%, respectively), compared to community hospitals, comprehensive cancer centres, and teaching hospitals, which showed no increased adoption. On further analysis, EM use remained high for low-stage cancers at Veterans Affairs and low-volume hospitals (24.0% and 19.1%, respectively), regardless of age or comorbidity, a pattern not shared by other practice settings. CONCLUSIONS: EM utilization remained low and stable last decade, regardless of disease or patient characteristics. Conversely, Veterans Affairs and low-volume hospitals led the trend in national EM adoption, particularly in men with low-stage cancers and limited life expectancies. The limitations of this dataset preclude any determination of the appropriateness of EM utilization. Nonetheless, further study is needed to identify factors influencing EM adoption to ensure its proper use in the future.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".