Clinical judgment versus lung allocation score in predicting lung transplant waitlist mortality
Bibliographic record
Abstract
Canadian lung transplant centers currently use a subjective and dichotomous "Status" ranking to prioritize waitlisted patients for lung transplantation. The lung allocation score (LAS) is an objective composite score derived from clinical parameters associated with both waitlist and post-transplant survival. We performed a retrospective cohort study to determine whether clinical judgment (Status) or LAS better predicted waitlist mortality. All adult patients listed for lung transplantation between 2007 and 2012 at three Canadian lung transplant programs were included. Status and LAS were compared in their ability to predict waitlist mortality using Cox proportional hazards models and C-statistics. Status and LAS were available for 1122 patients. Status 2 patients had a higher LAS compared to Status 1 patients (mean 40.8 (4.4) vs 34.6 (12.5), P = .0001). Higher LAS was associated with higher risk of waitlist mortality (HR 1.06 per unit LAS, 95% CI 1.05, 1.07, P < .001). LAS predicted waitlist mortality better than Status (C-statistic 0.689 vs 0.674). Patients classified as Status 2 and LAS ≥ 37 had the worst survival awaiting transplant, HR of 8.94 (95% CI 5.97, 13.37). LAS predicted waitlist mortality better than Status; however, the best predictor of waitlist mortality may be a combination of both LAS and clinical judgment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".