Race-Based Pulmonary Function Testing Correction in COPD Inhaler Therapy Trials: A Systematic Review
Bibliographic record
Abstract
Purpose: Race-based correction is widely utilized in clinical practice, but may contribute to overestimation of lung function, underdiagnoses in minority groups, and exclusion of minority groups from research trials. The aim of this systematic review is to examine the usage of race-based correction in pulmonary function testing (PFT) within chronic obstructive lung disease (COPD) research and its impact on the exclusion of minority groups from research trials. Methods: We systematically searched Medline from 2010 to 2022 to identify randomized controlled trials (RCTs) that examine inhaler therapy for COPD. Article screening, critical appraisal, and data extraction were completed in duplicate by independent reviewers. Data regarding study design, inclusion criteria, demographics, and race-based correction were extracted and synthesized narratively. Results: Of the 774 screened articles, we included 21 RCTs in the review, which were multinational trials involving 70696 study participants. All studies had an inclusion criteria of an FEV 1 cutoff of 50% to 80%. Racial minorities remained underrepresented in the trials, with the proportion of black participants ranging from < 1% to 4.7%. Four studies directly mentioned race-based correction, while the remainder of the studies did not provide any explicit details. After obtaining additional information by contacting authors and reviewing the citations, 15 were estimated to utilize race-based correction. Conclusion: Race-based correction may be frequently utilized in major COPD RCTs, but there remains inconsistent reporting regarding the usage of race-based correction. This may contribute to the exclusion of racialized populations from research trials as there remains significant underrepresentation of racialized populations from research. Keywords: chronic obstructive lung disease, pulmonary function testing, race-based correction, racial disparities, ethnic representation in research
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 teacher head, 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".