Development and external validation of a prognostic tool for prediction of cancer‐specific mortality after complete loco‐regional pathological staging for squamous cell carcinoma of the penis
Bibliographic record
Abstract
OBJECTIVE: To develop a novel postoperative prognostic tool, which attempts to integrate both pathological tumour stage and histopathological factors, for prediction of cancer-specific mortality (CSM) of squamous cell carcinoma of the penis (SCCP). PATIENTS AND METHODS: Patients with SCCP treated with inguinal lymph node dissection (ILND) or sentinel LN biopsy at a single institution were used for nomogram development and internal validation (n = 434), while a second cohort was used for external validation (n = 338). Multivariable Cox proportional hazards were used to examine the prognostic ability of patient age, a modified tumour staging that distinguishes between spongiosum and cavernosum body ingrowth tumours, a modified LN staging that integrates information on presence/absence of LN metastasis, extent of inguinal LN metastases, pelvic LN involvement, and extranodal involvement, and tumour grade. Model performance was quantified using measures of discrimination and calibration. RESULTS: Overall, 36% of patients had positive LN metastases (n = 156). In univariable analyses, the modified tumour and LN staging systems were statistically significantly associated with CSM, and remained in the final model with a discrimination of 89% within internal validation, and 95% within external validation. Calibration was nearly perfect. CONCLUSIONS: The newly developed model integrates important prognostic factors, which existing models do not consider. Its performance was highly accurate using measures of discrimination and calibration.
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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 | 0.015 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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 | 0.000 |
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".