Follow up study of “atypical” prostate needle core biopsies; the Winnipeg experience and literature review
Bibliographic record
Abstract
High grade intraepithelial neoplasia (HGPIN) and atypical small acinar proliferation (ASAP) are two pathological lesions associated with prostate adenocarcinoma. HGPIN is an architectural finding, while ASAP is a term used to describe a lesion that cannot confidently be diagnosed as prostate adenocarcinoma. The mean incidence rate for HGPIN is 7.7% with a median of 5.2% and range of 0-24.6% and the cancer detection rate mean is 18.1%. The mean incidence rate for ASAP is 5.0% with a median of 4.4% and a range of 0.7-23.4%. The mean cancer detection rate is 40.2%. Currently, the incidence and cancer detection rates for HGPIN and ASAP for Winnipeg, Manitoba, have not been published. A retrospective study was conducted on all prostate biopsies collected from the Manitoba Cancer Care Prostate Centre (MCCPC) from 2008 and 2009. Prostate biopsies with a diagnosis of isolated HGPIN and or ASAP and no previous history of cancer were included in this study. In Winnipeg, Manitoba, from 2008-2009, the mean HGPIN incidence rate was 5.0% and the mean cancer detection rate was 46.1%. The mean ASAP incidence rate was 4.6% and the mean cancer detection rate of 48.2%. As a control, the cancer detection rate following a benign diagnosis was also calculated at 33.3%. The mean incidence and cancer detection rates for HGPIN and ASAP for Winnipeg, Manitoba are slightly lower than literature, but still fall within the published range. In addition, the mean ASAP cancer detection rate is similar to the cancer detection rate following a benign diagnosis indicating that, in our study, both a benign finding and a diagnosis of ASAP hold the same predictive value for cancer on a subsequent re-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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".