Assessing tools for management of noncolorectal nonneuroendocrine liver metastases: External validation of a prognostic model
Bibliographic record
Abstract
INTRODUCTION: Selection criteria and benefits for resection of noncolorectal, nonneuroendocrine liver metastases (NCNNELM) remain debated. A prognostic score was developed by the Association Française de Chirurgie (AFC) for patient selection, but not validated. We performed a geographic external validation of this score. METHODS: Patients with resected NCNNELM from six institutions (2000-2014) were assigned risk groups based on the AFC score. Discrimination was evaluated by visually inspecting separation of overall survival (OS) curves among risk categories. The slope of the continuous score on OS and hazard ratios for risk categories were examined. RESULTS: Of 165 patients, 53 (32.1%) were low-risk, 85 (51.5%) intermediate-risk, and 27 (16.4%) high-risk. The OS curves did not separate among risk groups: 5-year OS were 60.1% (low), 57.1% (intermediate), and 55.6% (high). The parameter estimate (0.02) indicated lower discrimination than in the AFC cohort. Hazard ratios of 1.05 (0.63 to 1.70) for low vs intermediate, 0.87 (0.46 to 1.64) for low vs high, and 0.83 (0.46 to 1.49) for intermediate vs. high, demonstrated lack of discrimination in OS among risk groups. CONCLUSION: While long-term survival is achievable, discrimination of the AFC score is not maintained in a geographic external cohort of resected NCNNELM. It is not generalizable to this external population.
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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 | 0.028 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".