McGill Brisbane Symptom Score for patients with resectable pancreatic head adenocarcinoma
Bibliographic record
Abstract
AIM: To evaluate the ability of the McGill Brisbane Symptom Score (MBSS) to predict survival in resectable pancreatic head adenocarcinoma (PHA) patients. METHODS: All PHA patients (n = 83) undergoing pancreaticoduodenectomy at the McGill University Health Center, Quebec between 1/2001-1/2010 were evaluated. Data related to patient and cancer characteristics, MBSS variables, and treatment were collected; univariable and multivariable survival analyses were performed. We obtained complete follow-up until February 2011 in all patients through the database of the provincial health insurance plan of Quebec. The unique health insurance numbers of these patients were used to retrieve information from this database which captures all billable clinical encounters, and ensures 100% actual survival data. RESULTS: Median survival was 23 mo overall: 45 mo for patients with low MBSS, 17 mo for high MBSS (P = 0.005). At twelve months survival was 83.3% (95%CI: 66.6-92.1) vs 58.1% (95%CI: 42.1-71.2) in those with low vs high MBSS, and 24 mo survival was 63.8% (95%CI: 45.9-77.1) and 34.0% (95%CI: 20.2-48.2) respectively. In the multivariate Cox model (stratified by chemotherapy), after addition of clinically meaningful covariates, MBSS was the variable with the strongest association with survival (HR = 2.63; P = 0.001). Adjuvant chemotherapy interacted with MBSS category such that only high MBSS patients accrued a benefit. In univariate analysis we found a lower mortality in high MBSS but not low MBSS patients receiving adjuvant chemotherapy. This interaction variable, on Cox model, resulted in an adjusted mortality HR for the high MBSS (compared to low MBSS) of 4.14 (95%CI: 1.48-11.64) without chemotherapy and 2.11 (95%CI: 1.06-4.17) with chemotherapy. CONCLUSION: The MBSS is a simple prognostic tool for resectable PHA. Preoperative categorization of patients according to the MBSS allows effective stratification of patients to guide therapy.
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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 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".