Conditional survival predictions after surgery for patients with penile carcinoma
Bibliographic record
Abstract
BACKGROUND: Conditional survival (CS) implies that, on average, long-term cancer survivors have a better prognosis than newly diagnosed individuals. The objective of the current study was to devise an accurate predictive tool that accounts for CS in men diagnosed with penile cancer. METHODS: Overall, 1245 patients treated with primary tumor excision (PTE) for pT(1-3)M0 squamous cell carcinoma of the penis (SCCP) between 1998 and 2006 were identified. Cox regression models were fitted for prediction of cancer-specific mortality (CSM). Nomogram development for prediction of CSM using CS methodology at 2 and 5 years was performed on 670 patients. External validation and calibration of the conditional nomogram was performed in 575 patients. RESULTS: The 5-year CSM-free survival of patients at surgery was 84.3% and increased to 95.0% and 97.8% after 2 and 5 years of disease-free survival (DFS), respectively. The predicted probabilities varied by as much as 49% (57% vs 85%) when, for example, predictions of CSM-free survival at 5 years were made after PTE versus after 2 years of DFS. Within the external validation cohort, the accuracy of the conditional nomogram was 75.3% and 78.1% at 2 and 5 years after PTE. CONCLUSIONS: The authors developed and externally validated the first conditional nomogram for predicting SCCP CSM-free survival that allows consideration of the length of survivorship.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".