The Impact of Changing Race-Specific Equations for Lung Function Tests among Veterans with Chronic Obstructive Pulmonary Disease
Bibliographic record
Abstract
Abstract Rationale The American Thoracic Society recommended a single reference equation for spirometry, but the impact on patients is not known. Objectives To estimate the effect of changing to a single reference equation among veterans with chronic obstructive pulmonary disease (COPD). Methods A cross-sectional study was conducted including veterans aged ⩾40 to ⩽89 years with COPD and spirometry results from 21 facilities between 2010 and 2019. We collected race and ethnicity data from the electronic health record. We estimated the percentage change in the number of veterans with lung function meeting clinical thresholds used to determine eligibility for lung resection for cancer, lung volume reduction surgery (LVRS), and lung transplantation referral. We estimated the change for each level of U.S. Department of Veterans Affairs service connection and financial impact. Results We identified 44,892 veterans (Asian, 0.5%; Black, 11.8%; White, 80.8%; and Hispanic, 1.8%). When changing to a single reference equation, Asian and Black veterans had reduced predicted lung function that could result in less surgical lung resection (4.4% and 11.1%, respectively) while increasing LVRS (1.7% and 3.8%) and lung transplantation evaluation for Black veterans (1.2%). White veterans had increased predicted lung function and could experience increased lung resection (8.1%), with less LVRS (3.3%) and lung transplantation evaluation (0.9%). Some Asian and Black veterans could experience increases in monthly disability payments (+$540.38 and +$398.38), whereas White veterans could see a decrease (−$588.79). When aggregated, Hispanic veterans experienced changes attributable to their racial identity and, because this sample was predominantly Hispanic White, had similar results to White veterans. Conclusions Changing the reference equation could affect access to treatment and disability benefits, depending on race. If adopted, the use of discrete clinical thresholds needs to be reassessed, considering patient-centered outcomes.
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 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 | 0.022 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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 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".