Validation of predictors for lymph node status in penile cancer: Results from a population-based cohort
Bibliographic record
Abstract
INTRODUCTION: The ability to predict lymph node (LN) status is essential in the management of men with localized squamous cell carcinoma (SCC) of the penis. There has been limited external validation of available risk stratification tools, particularly in routine clinical care. The objective of this study was to evaluate the predictive variables of LN metastases within a large population-based cohort of patients. METHODS: In this population-based cohort study, surgical pathology reports were linked to the population-based Ontario Cancer Registry to identify all patients who were diagnosed with penile cancer in Ontario, Canada. Multivariable analyses were performed to evaluate predictive variables for LN involvement. Three contemporary risk stratification schemes used to predict LN status were analyzed by logistic regression. RESULTS: The study included 380 localized penile SCC cases treated between 2000 and 2010. Sixty-three (17%) had pathologically confirmed LN metastases. Among these, 35 (56%) were diagnosed within three months of the initial penile SCC diagnosis and these patients had a worse five-year disease-specific survival (43%; 95% confidence interval [CI] 26-64) compared to patients who were diagnosed at a delayed LN dissection. On multivariable analysis, age (odds ratio [OR] 0.68; 95% CI 0.52-0.88), pathological stage (≥pT1b; OR 3.32; 95% CI 1.38-8.01), and tumour grade (Grade 2 OR 2.98; 95% CI 1.26-7.62; Grade 3 OR 3.97; 95% CI 1.32-11.9) were associated with an increased risk of LN metastases. Candidate risk stratification schemes demonstrated moderate to good property, with C-statistics ranging from 0.662-0.747. CONCLUSIONS: Using a population-based cohort of penile cancer patients with a relatively low proportion of patients with pathologically confirmed LN involvement, we confirm and externally validate the importance of age, stage, and grade of the primary tumour in predicting nodal status.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".