Diagnosing asthma in children: What is the role for methacholine bronchoprovocation testing?
Bibliographic record
Abstract
OBJECTIVE: To determine whether measurement of airways responsiveness to methacholine can help physicians diagnose asthma in children. METHODS: Children from the 1995 Manitoba birth cohort were assessed by asthma specialists, had skin testing and measurement of airways responsiveness to methacholine (PC20). We selected children with doctor-diagnosed asthma and healthy children as controls (no asthma, no allergic rhinitis, negative skin tests). Sensitivities and specificities for asthma were calculated. Receiver operating curves were calculated to determine the best fit of the methacholine challenge as a diagnostic test. RESULTS: 640 children were assessed. Two hundred fifteen children with doctor diagnosed asthma and 197 healthy controls successfully completed a methacholine challenge. Airways hyperresponsiveness was a moderately sensitive and specific measure for the diagnosis of asthma in girls, whether atopic (sensitivity of 71% and specificity of 69% at PC20 < or = 4.0 mg/ml) or not (sensitivity of 77% and specificity of 53% at PC20 < or =8.0 mg/ml). Airways hyperresponsiveness was also helpful for the diagnosis of asthma in atopic boys (sensitivity of 67% and specificity of 75% at PC20 < or =2.0 mg/ml), but of absolutely no help in the diagnosis of asthma in nonatopic boys. CONCLUSION: Measurement of airways hyperresponsiveness to methacholine can be useful in children who are atopic and of some value in nonatopic girls. The presence or absence of airways hyperresponsiveness to methacholine is of no help for the diagnosis of asthma in nonatopic boys. Laboratory tests must be placed in context of the clinical assessment of children for asthma.
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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 | 0.008 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".