Dose omission to shorten methacholine challenge testing: clinical consequences and safety of the use of a "10% fall in FEV1" threshold
Bibliographic record
Abstract
Introduction: In methacholine challenge testing (MCT), skipping a methacholine dose is suggested if FEV1 falls by <5%. Using a larger threshold may shorten test duration, but data supporting this hypothesis is lacking. We evaluated the safety of using a 10% FEV1 fall as threshold to skip the next dose of methacholine in patients undergoing MCT. Methods: We reviewed MCTs performed in our center in 2017. A ≤10% FEV1 fall allowed the omission of the next methacholine dose. Patients of interest were those in which a dose was skipped after a previous FEV1 fall outside the usual range (5-10%, termed “Skip5-10%”). Adverse events (AE; mild: >1 salbutamol dose to reach basal FEV1, palpitations, cough; severe: hypoxemia, hospitalization) were compared in the Skip5-10% group and others. Regression analysis was used to identify predictors of AE. Results: 100 MCTs were analysed (40 males, age 52±14 years). Skip5-10% occurred in 39 tests, of which 6 directly led to the reaching of CP20. All AE were mild. Skip5-10% was not associated with more AE (8 vs 3%, p=0.32), except when it directly led to the CP20 (50 vs 6%, p=0.003). These patients had higher CP20 (6.3±2.9 vs 3.4±2.3 mg/ml, p<0.01) and baseline FEV1 (115±21 vs 93±13%, p<0.01). Predictors of AE were lower CP20 (p=0.02) and baseline FEV1 values (p=0.04), but not pre-test probability of asthma (p=0.24) or the use of Skip5-10% (p=0.34). Conclusion: Omitting a methacholine dose based on a ≤10% fall in FEV1 occurs frequently and has the potential to shorten test duration. AE were rare, but the small proportion of patients that immediately reach CP20 after dose skipping are at increased risk of mild side effects.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".