Montreal prognostic score: estimating survival of patients with non-small cell lung cancer using clinical biomarkers
Bibliographic record
Abstract
BACKGROUND: For evidence-based medical practice, well-defined risk scoring systems are essential to identify patients with a poor prognosis. The objective of this study was to develop a prognostic score, the Montreal prognostic score (MPS), to improve prognostication of patients with incurable non-small cell lung cancer (NSCLC) in everyday practice. METHODS: A training cohort (TC) and a confirmatory cohort (CC) of newly diagnosed patients with NSCLC planning to receive chemotherapy were used to develop the MPS. Stage and clinically available biomarkers were entered into a Cox model and risk weights were estimated. C-statistics were used to test the accuracy. RESULTS: The TC consisted of 258 patients and the CC consisted of 433 patients. Montreal prognostic score classified patients into three distinct groups with median survivals of 2.5 months (95% confidence interval (CI): 1.8, 4.2), 8.2 months (95% CI: 7.0, 9.4) and 18.2 months (95% CI: 14.0, 27.5), respectively (log-rank, P<0.001). Overall, the C-statistics were 0.691 (95% CI: 0.685, 0.697) for the TC and 0.665 (95% CI: 0.661, 0.670) for the CC. CONCLUSION: The MPS, by classifying patients into three well-defined prognostic groups, provides valuable information, which physicians could use to better inform their patients about treatment options, especially the best timing to involve palliative care teams.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".