A nomogram predicting subclinical extension of cutaneous squamous cell carcinoma in Chinese individuals
Bibliographic record
Abstract
ABSTRACT: Cutaneous squamous cell carcinoma usually extends beyond the visible margin. Little is known about the predictors for cutaneous squamous cell carcinoma with subclinical extension in Chinese individuals. This study aimed to construct a nomogram for predicting the probability of subclinical extension of cutaneous squamous cell carcinoma in Chinese patients.A retrospective analysis was conducted using data from Mohs micrographic surgery-treated cutaneous squamous cell carcinoma patients at a single institution between December 1, 2009 and October 31, 2019. Subclinical extension was defined as a lesion requiring ≥ 2 Mohs stages or with final safe margins of ≥ 5 mm. A nomogram predicting the probability of subclinical extension was constructed using the predictors identified in multivariable analysis.Of 274 patients included, 119 (43.4%) had subclinical extension. In multivariable analysis, male sex (odds ratio [OR], 2.45; 95% confidence interval [CI], 1.40-4.29; P = .002), lesions on mucocutaneous areas (OR, 3.71; 95% CI, 1.34-10.32; P = .012) and extremities (OR, 2.40; 95% CI, 1.20-4.78; P = .013), maximum diameter of 10 to 19 mm (OR, 14.15; 95% CI, 4.24-47.28; P < .001), and 20 to 29 mm (OR, 9.21; 95% CI, 2.80-30.29; P < .001) were associated with subclinical extension. A nomogram incorporating these 3 variables demonstrated promising predictive ability (C statistics = 0.78; 95% CI, 0.67-0.89).The nomogram incorporating sex, tumor location, and maximum diameter can provide individualized prediction for subclinical extension in Chinese patients with cutaneous squamous cell carcinoma. This information may help surgeons determine appropriate margins at the first Mohs stage.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".