Combined Reporting of Surgical Quality and Cancer Control after Surgical Treatment for Penile Tumors with Inguinal Lymph Node Dissection: The Tetrafecta Achievement
Bibliographic record
Abstract
Background: To optimize results reporting after penile cancer (PC) surgery, we proposed a Tetrafecta and assessed its ability to predict overall survival (OS) probabilities. Methods: A purpose-built multicenter, multi-national database was queried for stage I–IIIB PC, requiring inguinal lymphadenectomy (ILND), from 2015 onwards. Kaplan–Meier (KM) method assessed differences in OS between patients achieving Tetrafecta or not. Univariable and multivariable regression analyses identified its predictors. Results: A total of 154 patients were included in the analysis. The 45 patients (29%) that achieved the Tetrafecta were younger (59 vs. 62 years; p = 0.01) and presented with fewer comorbidities (ASA score ≥ 3: 0% vs. 24%; p < 0.001). Although indicated, ILND was omitted in 8 cases (5%), while in 16, a modified template was properly used. Although median LNs yield was 17 (IQR: 11–27), 35% of the patients had <7 nodes retrieved from the groin. At Kaplan–Maier analysis, the Tetrafecta cohort displayed significantly higher OS probabilities (Log Rank = 0.01). Uni- and multivariable logistic regression analyses identified age as the only independent predictor of Tetrafecta achievement (OR: 0.97; 95%CI: 0.94–0.99; p = 0.04). Conclusions: Our Tetrafecta is the first combined outcome to comprehensively report results after PC surgery. It is widely applicable, based on standardized and reproducible variables and it predicts all-cause mortality.
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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 | 0.013 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".