Development and external validation of a biopsy‐derived nomogram to predict risk of ipsilateral extraprostatic extension
Bibliographic record
Abstract
OBJECTIVES: To develop and externally validate a nomogram that predicts risk of side-specific extraprostatic extension (EPE) at time of surgery, using commonly available preoperative markers. MATERIALS AND METHODS: A consecutive sample of 753 men treated by radical prostatectomy (RP) at the University Health Network, Toronto, between 2009 and 2015, was used to develop the nomogram. The validation cohort consisted of 311 men treated by RP at Ottawa Hospital Research Institute, between 1992 and 2014. The study outcome was presence of ipsilateral EPE. The association between predictors considered and EPE was tested using univariate and multivariate logistic regression analyses. The predictive accuracy of the nomogram was determined using the area under the receiver-operating characteristic curve. RESULTS: The overall rate of EPE was 19.8% of all lobes in the developmental cohort and 28.9% in the validation cohort. Significant variables in the models were age, prostate-specific antigen and ipsilateral Gleason score, percentage of positive cores and highest core involvement (all P < 0.05). The nomogram predicting risk of EPE had a predictive accuracy of 0.74 in the external validation cohort. CONCLUSION: We developed and externally validated a nomogram that predicts the risk of ipsilateral EPE based on commonly used preoperative markers. This nomogram may be used to assist surgical decision-making prior to RP.
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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 | 0.017 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".