Bibliographic record
Abstract
Background: Continued prostate cancer screening with serum PSA in patients with a history of a negative prostate biopsy can result in unnecessary repeat prostate biopsies with significant morbidity and cost.The PCA3 molecular urine test has been shown to an independent predictor for the diagnosis of significant prostate cancer in men who are still at risk after a negative prostate biopsy.We utilized a budget impact model to study the potential reduction in unnecessary prostate biopsies and its cost benefits in this population Methods: In a theoretical population of 1 million people over a oneyear timeline, the number of prostate biopsies and its cost was compared between a "Traditional" method of prostate cancer screening (e.g., PSA screening) after an initial negative prostate biopsy and an "New" method incorporating the PCA3 urine test after an elevated PSA.Men with abnormal results would undergo repeat prostate biopsy Results: In the "Traditional" method, 959 repeat prostate biopsies would be performed at a fully weighted cost of $1,866,214.When PCA3 was used in the "New" method, 400 prostate biopsies would be performed at a fully weighted cost of $931,000.There was a cost savings of $935,214.Conclusions: The incorporation of the PCA3 urine test into our decision algorithm for men at risk for prostate cancer after an initial negative prostate biopsy will result in a significant reduction in unnecessary biopsies with significant cost savings P66 Radiotherapy With Androgen Deprivation Therapy For Highrisk Prostate
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.737 | 0.490 |
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".