Ratio of Maximal Inspiratory to Expiratory Flow Aids in the Separation of COPD from Asthma
Bibliographic record
Abstract
Patients who have chronic obstructive pulmonary disease (COPD) and bronchial asthma (BA) share symptoms such as, dyspnoea, cough and wheeze. Differentiating these diseases in the ambulatory setting can be challenging especially in older adult smokers who are being treated with a variety of medications. The objective of this study was to test the value of adding a maximal inspiratory manoeuvre to basic spirometry to differentiate COPD and BA. One hundred forty-three COPD patients and 142 BA patients had measurements of maximal inspiratory and expiratory flow during routine spirometry. Parameters from these tests were used to assess diagnostic accuracy using receiver-operating characteristic (ROC) analyses followed by logistic regression. The association of two independent parameters were analyzed using linear regression analyses. Results show that forced expiratory volume in one second/forced vital capacity (FEV1/FVC%) <62.4 was the best independent predictor to diagnose COPD. The combination of FEV1/FVC% <62.4 and the ratio of peak inspiratory flow/maximal expiratory flow at 50% FVC (PIF/MEF50) >3.06 significantly predicted COPD. Post-test probability for prediction of COPD was 82.0% when patients had both parameters. When asthmatic patients with a smoking history were compared with COPD patients, FEV1/FVC% <63.4 and PIF/MEF50 >3.29 were both independent predictors of COPD. The post-test probability for COPD was 94.4% when patients had both parameters. The association between FEV1/FVC% and PIF/MEF50 was significantly different between COPD and BA. In conclusion, the addition of the maximal inspiratory effort to routine pulmonary function measurements provides a simple test to help differentiate COPD and BA.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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; 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".