Risk Prediction Models for Sentinel Node Positivity in Melanoma
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 it