Addressing Race in Pulmonary Function Testing by Aligning Intent and Evidence With Practice and Perception
Bibliographic record
Abstract
The practice of using race or ethnicity in medicine to explain differences between individuals is being called into question because it may contribute to biased medical care and research that perpetuates health disparities and structural racism. A commonly cited example is the use of race or ethnicity in the interpretation of pulmonary function test (PFT) results, yet the perspectives of practicing pulmonologists and physiologists are missing from this discussion. This discussion has global relevance for increasingly multicultural communities in which the range of values that represent normal lung function is uncertain. We review the underlying sources of differences in lung function, including those that may be captured by race or ethnicity, and demonstrate how the current practice of PFT measurement and interpretation is imperfect in its ability to describe accurately the relationship between function and health outcomes. We summarize the arguments against using race-specific equations as well as address concerns about removing race from the interpretation of PFT results. Further, we outline knowledge gaps and critical questions that need to be answered to change the current approach of including race or ethnicity in PFT results interpretation thoughtfully. Finally, we propose changes in interpretation strategies and future research to reduce health disparities.
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 | 0.255 | 0.497 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.003 | 0.016 |
| Scholarly communication | 0.012 | 0.021 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.007 | 0.014 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".