Impact of Body Mass Index on the Risk of Exacerbation in Patients With COPD: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Objective: The objective of this review is to synthesize current evidence of the association between body mass index (BMI) categories and the risk of exacerbation in patients with chronic obstructive pulmonary disease (COPD). Methods: A systematic search was conducted across 3 electronic databases: PubMed, Embase, and Scopus. Eligible studies must have reported on the association between BMI (either as continuous or categorical) and risk of COPD exacerbation, as defined according to recognized clinical criteria. Observational studies (cohort, case-control, cross-sectional) were eligible for inclusion. The Newcastle Ottawa Scale (NOS) was used to evaluate the methodological quality. Combined effect sizes were reported as relative risk (RR) and corresponding 95% confidence intervals (CI). Results: =94.2%). Overweight and obese BMI status was associated with a similar risk of exacerbation. Conclusion: Our findings report that underweight, but not overweight or obese patients, have an increased risk of COPD exacerbation, compared to individuals with a normal BMI. This differential association emphasizes the need for nuanced investigations into the underlying mechanisms of the impact of BMI on the course of COPD. Further research is needed to inform personalized interventions and improve COPD management strategies.
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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 | 0.009 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.023 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".