The presence of prostate cancer on saturation biopsy can be accurately predicted
Bibliographic record
Abstract
OBJECTIVE: To improve the ability of our previously reported saturation biopsy nomogram quantifying the risk of prostate cancer, as the use of office-based saturation biopsy has increased. PATIENTS AND METHODS: Saturation biopsies of 540 men with one or more previously negative 6-12 core biopsies were used to develop a multivariable logistic regression model-based nomogram, predicting the probability of prostate cancer. Candidate predictors were used in their original or stratified format, and consisted of age, total prostate-specific antigen (PSA) level, percentage free PSA (%fPSA), gland volume, findings on a digital rectal examination, cumulative number of previous biopsy sessions, presence of high-grade prostatic intraepithelial neoplasia on any previous biopsy, and presence of atypical small acinar proliferation (ASAP) on any previous biopsy. Two hundred bootstraps re-samples were used to adjust for overfit bias. RESULTS: Prostate cancer was diagnosed in 39.4% of saturation biopsies. Age, total PSA, %fPSA, gland volume, number of previous biopsies, and presence of ASAP at any previous biopsy were independent predictors for prostate cancer (all P < 0.05). The nomogram was 77.2% accurate and had a virtually perfect correlation between predicted and observed rates of prostate cancer. CONCLUSIONS: We improved the accuracy of the saturation biopsy nomogram from 72% to 77%; it relies on three previously included variables, i.e. age, %fPSA and prostate volume, and on three previously excluded variables, i.e. PSA, the number of previous biopsy sessions, and evidence of ASAP on previous biopsy. Our study represents the largest series of saturation biopsies to date.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".