Pulmonary Function Reference Equations: A Brief History to Explain All the Confusion
Bibliographic record
Abstract
Predicted values for pulmonary function tests differ significantly from the reference values used for many other diagnostic tests. Historically, simple equations using age, height, and sex were used to "predict" normal lung function. However, these multiple factors interact in complex ways to determine what the expected lung function values are in healthy subjects. Healthy individuals exhibit a wide range of variability for most pulmonary function variables, and this variability is not consistent across all age ranges. Recent analysis of large groups of healthy subjects has allowed the development of sophisticated prediction models that take into account not only variability but also skew that occurs as the lungs develop and mature. These modern reference equations provide uninterrupted expected values from early childhood, through adolescence and adulthood, and extending into the ninth decade. Modern equations use upper and lower limits of normal to offer a statistically robust means of defining who is within normal limits. Despite these advances, interpretation of pulmonary function test results has not been highly standardized, largely because interpretation depends on the reference equations used and, more importantly, how they are applied. This review discusses the strengths and limitations of using reference equations to interpret pulmonary function data in the context of research and clinical practice.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads agree on what is shown here.
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".