Risk Prediction Models for Sentinel Node Positivity in Melanoma
Bibliographic record
Abstract
Importance: There is a need to identify the best performing risk prediction model for sentinel lymph node biopsy (SLNB) positivity in melanoma. Objective: To comprehensively review the characteristics and discriminative performance of existing risk prediction models for SLNB positivity in melanoma. Data Sources: Embase and MEDLINE were searched from inception to May 1, 2024, for English language articles. Study Selection: All studies that either developed or validated a risk prediction model (defined as any calculator that combined more than 1 variable to provide a patient estimate for probability of melanoma SLNB positivity) with a corresponding measure of model discrimination were considered for inclusion by 2 reviewers, with disagreements adjudicated by a third reviewer. Data Extraction and Synthesis: Data were extracted in duplicate according to Data Extraction for Systematic Reviews of Prediction Modeling Studies, Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis, and Preferred Reporting Items for Systematic Reviews and Meta-Analyses reporting guidelines. Effects were pooled using random-effects meta-analysis. Main Outcome and Measures: The primary outcome was the mean pooled C statistic. Heterogeneity was assessed using the I2 statistic. Results: In total, 23 articles describing the development of 21 different risk prediction models for SLNB positivity, 20 external validations of 8 different risk prediction models, and 9 models that included sufficient information to obtain individualized patient risk estimates in routine preprocedural clinical practice were identified. Among all risk prediction models, the pooled weighted C statistic was 0.78 (95% CI, 0.74-0.81) with significant heterogeneity (I2 = 97.4%) that was not explained in meta-regression. The Memorial Sloan Kettering Cancer Center and Melanoma Institute of Australia models were most frequently externally validated with both having strong and comparable discriminative performance (pooled weighted C statistic, 0.73; 95% CI, 0.69-0.78 vs pooled weighted C statistic, 0.70; 95% CI, 0.66-0.74). Discrimination was not significantly different between models that included gene expression profiles (pooled C statistic, 0.83; 95% CI, 0.76-0.90) and those that only used clinicopathologic features (pooled C statistic, 0.77; 95% CI, 0.73-0.81) (P = .11). Conclusions and Relevance: This systematic review and meta-analysis found several risk prediction models that have been externally validated with strong discriminative performance. Further research is needed to evaluate the associations of their implementation with preprocedural care.
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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 | 0.048 | 0.181 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.017 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".