Risk factors analysis and nomogram for predicting recurrence in periocular basal cell carcinoma
Bibliographic record
Abstract
OBJECTIVES: Aim to develop a nomogram to effectively predict the potential for recurrence after surgical resection in patients with periocular basal cell carcinoma (BCC). METHODS: We conducted a retrospective study involving 329 patients with eyelid BCC. Univariate and multivariate Cox proportional risk regression was used to screen for independent factors affecting BCC recurrence. Kaplan-Meier survival curve analysis was performed to evaluate their impact on prognosis. On the basis of the results obtained from Cox regression analysis, a nomogram was established for the 1-, 2-, and 3-year recurrence-free survival (RFS) rates of BCC. RESULTS: In this study, a total of 15 patients out of 329 patients (4.6%) developed local recurrence. Multivariate analysis revealed that age, pathological type, previous history of BCC, and the number of surgeries were independent risk factors for BCC recurrence (p < 0.05, respectively). These risk factors were utilized to construct a nomogram to predict postoperative recurrence for these patients. The C-index of the nomogram was 0.867 (95% CI: 0.817-0.916), and the receiver operating characteristic curves were used to assess the discriminatory degree of the nomogram, with area under the curve values of 0.978, 0.870, and 0.916 at 1, 2, and 3 years, respectively. The calibration curves were basically fitted to the ideal curves. CONCLUSIONS: Age, pathological type, previous history of BCC, and the number of surgeries are significant risk factors for periocular BCC recurrence. Establishing a nomogram related to recurrence risk factors can more accurately predict the recurrence-free survival of individual patients.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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".