CA 19-9 and the McGill Brisbane Symptom Score: predictors of pancreatic cancer survival
Bibliographic record
Abstract
Background: Clinical tools that predict pancreatic adenocarcinoma (PAC) survival to help tailor treatments are lacking.Our surgical group has developed a clinical score, the McGill Brisbane Symptom Score (MBSS) that predicts PAC survival in resectable and non--resectable PAC.CA 19--9, a biomarker used in the diagnosis of PAC, has demonstrated increased potential as a predictor of PAC survival.Objectives: To determine if the Pancreatic Adenocarcinoma Survival Score (PACSS), a combined score of the CA 19--9--to--bilirubin ratio and the MBSS, better predicts survival in patients with resectable pancreatic cancer compared to the MBSS alone.Methods: A retrospective chart review of 122 patients treated at the McGill University Health Center (MUHC) and the University Hospital Zurich (UHZ) was undertaken.For all patients we calculated the MBSS and the PACSS at the time of diagnosis and ascertained the 2--year survival.Results: Both the MBSS and the PACSS were strong predictors of survival with Hazard Ratios (HR) of 2.58 (95%CI 1.35--4.91)and 3.06 (95%CI 1.64 --5.70), respectively.Adding the patient age and sex, two other variables available at the time of diagnosis did not significantly improve the predictive ability of the models containing either the PACSS or the MBSS. Conclusions: Adding the CA 19--9--to--bilirubin ratio to the MBSS to form the PACSS may improve the predictive ability when compared to the MBSS alone.However the overlap in the 95% confidence intervals does not allow us to conclude that the difference is statistically significant.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".