Can spirometric norms be set using pre- or post- bronchodilator test results in older people?
Bibliographic record
Abstract
BACKGROUND: Chronic Obstructive Pulmonary Disease (COPD) is defined by post-bronchodilator spirometry. Data on "normal values" come predominantly from pre-bronchodilator spirometry. The effects of this on diagnosis are unknown. METHODS: Lower limits of normal (LLN) were estimated from "normal" participants in the Burden of Obstructive Lung Disease (BOLD) programme. Values separately derived using pre- and post-bronchodilator spirometry were compared. Sensitivity and specificity of criteria derived from pre-bronchodilator spirometry and pre-bronchodilator spirometry adjusted by a constant were assessed in the remaining population. The "gold standard" was the LLN for the post-bronchodilator spirometry in the "normal population". For FEV1/FVC, sensitivity and specificity of criteria were also assessed when a fixed value of < 70% was used rather than LLN. RESULTS: Of 6,600 participants with full data, 1,354 were defined as "normal". Mean differences between pre- and post- bronchodilator measurements were small and the Bland-Altman plots showed no association between difference and mean value. Compared with using the gold standard, however, tests using pre-bronchodilator spirometry had a sensitivity and specificity of detecting a low FEV1 of 78.4% and 100%, a low FVC of 99.8% and 99.1% and a low FEV1/FVC ratio of 65% and 100%. Adjusting this by a constant improved the sensitivity without substantially altering the specificity for FEV1 (99%, 99.8%), FVC (97.4%, 99.9%) and FEV1/FVC (98.7%, 99.5%). CONCLUSIONS: Using pre-bronchodilator spirometry to derive norms for lung function reduces sensitivity compared to a post-bronchodilator gold standard. Adjustment of these values by a constant can improve validity of the test.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".