Contrasting GOLD and STAR classifications of spirometric COPD severity: relationship with clinical exercise testing outcomes
Bibliographic record
Abstract
Background: It has been postulated that grading the spirometric COPD severity by FEV1/FVC (STAR) would be superior to the traditional FEV1 % predicted approach endorsed by GOLD (AJRCCM 2023;208:676). Aim: To investigate whether STAR would better classify patients’ severity compared to GOLD vis-à-vis mechanical-ventilatory abnormalities and exertional dyspnoea during incremental CPET in COPD of varied severity. Methods: 359 patients (197 ♂, aged 41-86, FEV1 17-125 % pred) underwent an incremental CPET with serial inspiratory capacity (IC) measurements. An AI-based software (DyVe-X) determined the overall burden of sex- and age-adjusted metrics of dynamic submaximal (dyn) exertional dyspnea (Borg 0-10), ventilatory reserve (VRdyn), and inspiratory reserve (IRdyn). Results: There was a significant disagreement between the classifications, particularly in patients showing intermediate FEV1, i.e., GOLD 2-3 (p<0.05). Overall, ~ 30% (107/359) were classified as less impaired by STAR than GOLD (STAR 1 than STAR>GOLD (~ 20%, 75/359). Although the latter subjects tended to present with worse CPET outcomes, this was also the case for STAR p>0.05) (Figure). erj;66/suppl_69/PA5179/F1 F1 F1 Conclusions: STAR classification of COPD impairment did not consistently outperform GOLD’s FEV1% pred in grading the severity of exertional sensory and physiological abnormalities.
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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 | 0.011 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".