Prognostic value of various morphometric measurements of tumour extent in prostate needle core tissue
Bibliographic record
Abstract
AIMS: Predicting prostatic cancer patients' outcome is a major objective for clinicians and patients. Several nomograms are currently implemented prior to treatment to help predict clinical and pathological outcome. The aim of this study was to investigate the prognostic significance of morphometric measurements of cancer on the needle biopsy specimen in relation to the final pathological stage or the biochemical failure status following radical prostatectomy, and to determine which measurement of tumour length in cases with discontinuous foci of cancer (DFC) is most reliably reflective of the pathological stage. METHODS AND RESULTS: Of the 100 patients included in this study, 34% had high-stage disease (pT >or= 3 and/or pN1) and 16% experienced biochemical recurrence. The analysis showed that fraction of positive cores, total percentage of cancer and both total and greatest millimetric cancer lengths were the variables most closely associated with pathological stage and biochemical failure status. CONCLUSIONS: This study confirms the prognostic value of recording tumour extent in prostatic needle biopsy reporting. However, the results are inconclusive in determining the best method to record tumour length in cores with DFC and larger studies are needed to answer this question fully.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.001 | 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".