Blood Eosinophils and Pulmonary Rehabilitation in COPD
Bibliographic record
Abstract
Background. Blood eosinophils predict the response to therapy, risk of exacerbation, and readmission in COPD. This study investigates whether blood eosinophils predict pulmonary rehabilitation (PR) outcomes in COPD. Methods. We categorized patients into eosinophilic (blood eosinophils ≥300 cells/ml) or noneosinophilic (<300 cells/ml). In a retrospective design, we compared changes within and between the two groups on BODE index, 6-minute walk test (6MWT), FEV1, and mMRC dyspnea scale. Results. Of 206 patients enrolled, 176 were included for analysis; 90 were eosinophilic. BODE index improved in both groups: (MD −1.25; 95% CI (−0.45, −4.25), P ≤ 0.001 ) in the eosinophilic and (MD −1.33; 95% CI (−1.72, −0.94), P ≤ 0.001 ) in the noneosinophilic, but a higher BODE index remained in the eosinophilic (4.98); adjusted mean change (β): 0.7 (95% CI (0.15, 1.26), P = 0.01 ). 6MWT improved by 29.3 m in the eosinophilic (95% CI (14.2, 44.4), P ≤ 0.001 ) vs. 115.1 m in the noneosinophilic (95% CI (−30.4, 260.6), P = 0.12 ). FEV1 did not change in the eosinophilic (MD −0.6; 95% CI (−2.64, 1.48), P = 0.58 ), but improved by 2.5% in the noneosinophilic (MD 2.5; 95% CI (0.77, 4.17), P = 0.005 ). There were no significant between-group differences in 6MWT and FEV1; adjusted mean changes (β) were −9.69 m (95% CI (−39.51, 20.14), P = 0.52 ) and −2.31% (95% CI (−5.69, 1.08), P = 0.18 ), respectively. There were no significant within- or between-group changes in the mMRC scale. Conclusion. Although PR improves the BODE index in both eosinophilic and noneosinophilic COPD, a higher eosinophil count (≥300 cells/ml) is associated with a higher (worse) BODE index. Blood eosinophils may predict PR outcomes.
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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 | 0.000 | 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) | 0.002 | 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".