Does FEV1/FVC STAR relate better to sensory-physiological responses to exercise than FEV1 z-scores in patients with COPD?
Bibliographic record
Abstract
Background: Gradation of obstruction severity by FEV1 z-scores has been endorsed by ATS/ERS (ERJ 2022;60:2101499). More recently, the FEV1/FVC ratio (STAR classification) has been postulated to better categorize disablement than FEV1 in COPD (AJRCCM 2023;208:676). Aim: To investigate whether STAR would better grade dynamic (dyn) ventilatory abnormalities and dyspnoea compared to z-scores (A:>-1.645, B: -1.645 to -2.5; C: -2.51 to -4.0; and D: < -4 ) based on GLI standards in COPD of varied severity. Methods: 354 patients (196 ♂, 38-86 yrs, FEV1 17-125 %) underwent an incremental CPET with inspiratory capacity measurements. An AI-based software (DyVe-X) determined the overall burden of sex- and age-adjusted submaximal dyspnoeadyn (Borg 0-10), ventilatory reserve (VRdyn), and inspiratory reserve (IRdyn). Results: STAR and z-scores significantly disagreed, particularly in patients with intermediate dysfunction (p<0.05). Overall, ~30% (112/354) were classified as less impaired by STAR than z-score (STAR 2), more frequently women (59 vs 27%), and showed lower lung volumes (p<0.05). These patients did not show (p>0.05) less impaired exercise responses compared to STAR>z-score. erj;66/suppl_69/PA5191/F1 F1 F1 Conclusions: STAR is not superior to z-score in grading the severity of sensory and physiological abnormalities during exercise in mild-very severe COPD.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".