Competing‐risks analysis in patients with T1 squamous cell carcinoma of the penis
Bibliographic record
Abstract
What's known on the subject? and What does the study add? The European Association of Urology (EAU) guidelines recommend an inguinal lymph node dissection (ILND) in patients with T1G2‐3 squamous cell carcinoma of the penis (SCCP). To date, only four series reported the rates of cancer‐specific mortality (CSM) after primary tumor excision (PTE) without an ILND in patients with T1 clinically node‐negative (cN0) SCCP. We examined CSM rates in cN0 patients with T1G1‐3 SCCP, in whom an ILND was not performed, relying on competing‐risks analyses. OBJECTIVE To quantify and compare cancer‐specific mortality (CSM) and other‐cause mortality (OCM) in individuals with stage T1G1–3 clinically node‐negative (cN0) squamous cell carcinoma of the penis (SCCP) since there is no consensus regarding the need for an inguinal lymph node dissection (ILND) in patients with T1G2–3 cN0 SCCP. METHODS Relying on the Surveillance, Epidemiology and End Results database, we identified 655 patients diagnosed with primary SCCP between 1988 and 2006. Cumulative incidence plots were used to graphically depict the effect of CSM relative to OCM. Competing‐risks regression analyses were used to quantify the risk of CSM or OCM after adjusting for age, race, tumour grade and surgery type. RESULTS The 5‐year CSM rates after a primary tumour excision without ILND were 2.6%, 10.0% and 15.9% in patients with respectively T1G1, T1G2 and T1G3 cN0 SCCP. The 5‐year OCM rates were 29.5%, 27.3% and 29.3% in patients with respectively T1G1, T1G2 and T1G3. Age failed to provide additional stratification. CONCLUSIONS The CSM rate was highest in T1G3 patients and appears to justify ILND. Conversely, the CSM rate was lowest in T1G1 patients, which justifies active surveillance in this patient subset. A moderate CSM rate at 5 years was recorded for T1G2 patients, which brings into question the benefits of ILND.
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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 | 0.010 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| 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) | 0.002 | 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 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".