Inconsistent calculation methodology for the eucapnic voluntary hyperpnoea test affects the diagnosis of exercise-induced bronchoconstriction
Bibliographic record
Abstract
Introduction The eucapnic voluntary hyperpnoea (EVH) challenge is used to screen for exercise-induced bronchoconstriction. Several criteria have been proposed to determine the decrease in lung function (fall index, FI) following EVH. We compared three published FI calculation methods to determine if they affect the diagnostic classification. Methods The three FIs were calculated for 126 EVH tests. Spirometry was performed in duplicate at baseline and repeated 3, 5, 10, 15 and 20 min following 6 min of EVH. The higher of the two forced expiratory volume in 1 s (FEV1) measures at all time-points post-hyperpnoea was selected for the calculation of the FIs. The FIA was determined as the single lowest of the five postchallenge values, and a test was considered positive if FEV1 decreased ≥10 %. In FIB, a test was considered positive if FEV1 decreased ≥10% at two consecutive post-challenge time-points. The FIC was calculated identically to FIA, but was normalised to the achieved minute ventilation during the EVH challenge. Results Calculation method affected the raw FIs with FIB generating the smallest and FIC generating the highest values (p<0.001) and a within-subject range of 7%±10%. The number of positive tests differed between the calculation criteria: FIA: 62, FIB: 48 and FIC: 70, p<0.001. Nineteen participants (15%) tested positive in one or two FI methods only, indicating that the FI method used determined whether the test was positive or negative. Discussion Inconsistency in methodology of calculating the FI leads to differences in the diagnostic rate of the EVH test, with potential implications in both treatment and research outcomes.
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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 | 0.021 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".