The A.L.A.N. score identifies prognostic classes in advanced biliary cancer patients receiving first-line chemotherapy
Bibliographic record
Abstract
BACKGROUND: Chemotherapy is the mainstay treatment for advanced biliary cancer (ABC). Best supportive care and clinical trials are currently alternative options. The identification of a prognostic score that can be widely applied to daily practice has the potential to better inform clinical management of ABC patients. METHODS: A cohort of 123 ABC patients undergoing first-line chemotherapy was used as an exploratory cohort to define the prognostic value of laboratory tests routinely performed in clinical practice. Kaplan-Meier analysis was used to investigate the association between the variables and overall survival (OS). Those variables that were statistically significant at the multivariate analysis were combined in a multiplex score. Performance of the novel prognostic score was confirmed in a validation cohort of 60 ABC patients. RESULTS: Baseline actual neutrophil count, lymphocytes-monocytes ratio, neutrophil-lymphocytes ratio and albumin (A.L.A.N.) correlated with OS at the multivariate analysis in the exploratory cohort. When combined in the multiplex, A.L.A.N. score was able to identify three classes of ABC patients with significantly different OS (high-risk: median OS, 5 months; intermediate-risk: median OS, 12 months and low-risk: median OS, 22 months; p:<0.001). The score performed well in the different subtypes of ABC and was independent of stage, performance status and chemotherapy regimen. The performance of the A.L.A.N. score was confirmed in a validation cohort of cholangiocarcinoma patients (high-risk: median OS, 4.3 months; intermediate-risk: median OS 9.3 months, low-risk: median OS 13 months; p:0.005). CONCLUSIONS: The A.L.A.N score can be derived by variables routinely recorded in clinical practice and can provide prognostic assessment of ABC patients considered for first-line treatment.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".