Spirometric classifications of COPD severity as predictive markers for clinical outcomes: the HUNT Study
Bibliographic record
Abstract
ABSTRACT Rationale GOLD grades based on percent-predicted FEV 1 poorly predicts mortality. Studies have recommended alternative expressions of FEV 1 for the classification of COPD severity and they warrant investigation. Objective To compare the predictive abilities of ppFEV 1 (ppFEV 1 quartiles, GOLD grades, ATS/ERS grades), FEV 1 z-score (FEV 1 z-score quartiles, FEV 1 z-score grades), FEV 1 .Ht -2 (FEV 1 .Ht -2 quartiles, FEV 1 .Ht -2 grades), FEV 1 .Ht -3 (FEV 1 .Ht -3 quartiles), and FEV 1 Q (FEV 1 Q quartiles) to predict clinical outcomes. Methods People aged ≥40 years with COPD (n=890) who participated in the HUNT Study (1995-1997) were followed for 5 years (short-term) and up to 20.4 years (long-term). Survival analysis and time-dependent area under curve (AUC) were used to compare the predictive abilities. A regression tree approach was applied to obtain optimal cut-offs of different expressions of FEV 1 . The UK Biobank (n=6495) was used as a replication cohort with a 5-year follow-up. Results As a continuous variable, FEV 1 Q had the highest AUCs for all-cause mortality (short-term 70.2, long-term 68.3), respiratory mortality (short-term 68.4, long-term 67.7), cardiovascular mortality (short-term 63.1, long-term 62.3), COPD hospitalization (short-term 71.3, long-term 70.9), and pneumonia hospitalization (short-term 67.8, long-term 66.6), followed by FEV 1 .Ht -2 or FEV 1 .Ht -3 . Generally, similar results were observed for FEV 1 Q quartiles. The optimal cut-offs of FEV 1 Q had higher AUCs compared to GOLD grades for predicting short-term and long-term clinical outcomes. Similar results were found in UK Biobank. Conclusions FEV 1 Q best predicted the clinical outcomes and could improve the classification of COPD severity.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".