Population-based validation of the National Cancer Comprehensive Network recommendations for baseline imaging workup of cutaneous melanoma
Bibliographic record
Abstract
The aim of the current study is to assess the performance of some of the imaging scans recommended in the National Comprehensive Cancer Network Guidelines as part of baseline staging for cutaneous melanoma, regarding the detection of lung, brain, bone, and liver metastases. Surveillance, Epidemiology and End Results database (2010-2015) was used to extract the data, and cases with cutaneous melanoma and complete information about TN stages and sites of distant metastases were explored. Performance parameters assessed in the current study included positive predictive value (PPV), negative predictive value, sensitivity, specificity, number needed to investigate (NNI), and accuracy. A total of 109 971 patients were included in the analysis. If all stage III patients in the study cohort are to be staged through routine imaging, PPV (for the recognition of lung metastases) will be 2.9% and NNI to detect one case of lung metastasis will be 34. Likewise, PPV (for the recognition of bone metastases) will be 1.8% and NNI to detect one case of bone metastasis will be 55. Moreover, PPV (for the recognition of liver metastases) will be 1.8% and NNI to detect one case of liver metastasis will be 55. Excluding stage III patients with clinically node-negative/sentinel node-positive disease would improve PPV and decrease NNI for the three metastatic sites. Adherence to current National Comprehensive Cancer Network guidelines for cutaneous melanoma imaging for baseline staging results in low rates of failure to detect asymptomatic lung, liver, brain, or bone metastases.
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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 | 0.014 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.001 | 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".