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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.002 | 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 teacher head, 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".