Variation in patterns of practice in diagnosing screen‐detected prostate cancer
Bibliographic record
Abstract
OBJECTIVE: To determine the practice pattern of repeat prostate biopsies to detect prostate cancer, as there is growing evidence to support the recommendation that a repeat prostate biopsy should be taken after an initially negative prostate biopsy, the rate of cancer detection then being approximately 30%. PATIENTS AND METHODS: We examined the practice patterns of taking a repeat prostate biopsy after an initial negative biopsy and the predictors for cancer at repeat biopsy among 1536 patients who had an initial prostate biopsy because of an elevated prostate-specific antigen (PSA) level (>4.0 ng/mL) or abnormal digital rectal examination. RESULTS: Of the 1536 men, 712 (46.4%) had cancer detected on the first biopsy; of the remaining 824 with no cancer detected, 268 (32.5%) had a repeat biopsy within a year, and 68 of these (25.4%) had cancer detected. Of the cancers detected at repeat biopsy, 31% were high-grade. Men with abnormal histology (prostatic intraepithelial neoplasia or atypia) had an odds ratio of 3.2 (P < 0.001) for having a repeat biopsy. For men with normal initial prostate histology, those with an initial PSA of 10.0-20.0 and >20.0 ng/mL had an odds ratio of 3.6 and 4.5 (both P < 0.001), respectively, for a repeat prostate biopsy, compared with patients with a PSA of <10.0 ng/mL. However, the PSA level was not predictive of prostate cancer at repeat biopsy, but age and prostate volume were. CONCLUSIONS: A third of patients had a repeat biopsy after a negative biopsy. The most important factors influencing whether a patient was to have a repeat biopsy were initial biopsy histology and PSA level. However, the latter was not an important factor for predicting prostate cancer at repeat biopsy.
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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 | 0.003 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".