Airway hyperresponsiveness to methacholine in 7‐year‐old children: sensitivity and specificity for pediatric allergist‐diagnosed asthma
Bibliographic record
Abstract
BACKGROUND: The operating characteristics of PC(20) values used as cut-offs to define airway hyperresponsiveness, as it informs the diagnosis of asthma in children, are poorly understood. We examine data from a unique cohort to inform this concern. OBJECTIVE: Determine the sensitivity and specificity of incremental PC(20) cut-offs for allergist-diagnosed asthma. METHODS: Airway reactivity at age 7 was assessed in children within a birth cohort at high risk for asthma; PC(20) for methacholine was determined by standard technique including interpolation. The diagnosis of asthma was considered by the pediatric allergist without knowledge of the methacholine challenge results. Sensitivity and specificity were calculated using a cross-tabulation of asthma diagnosis with incremental PC(20) cut-off values, from 1.0 to 8.0 mg/ml, and plotted as receiver operator characteristic (ROC) curves. The "optimal" cut-off was defined as that PC(20) conferring maximal value for sensitivity plus specificity while the "balanced" cut-off was defined as that PC(20) at which sensitivity and specificity were most equal. RESULTS: 70/348 children (20.1%) were diagnosed with asthma. The optimal and balanced PC(20) cut-offs, both for all children and for females alone, were respectively 3 mg/ml (sensitivity 80.0%, specificity 49.1%) and 2 mg/ml (sensitivity 63.1%, specificity 64.7%). For males alone, the "optimal" and "balanced" PC(20) cut-offs were both 2 mg/ml. CONCLUSION: For this cohort of 7-year olds at high risk for asthma, methacholine challenge testing using a cut-off value of PC(20) 3 mg/ml conferred the maximal sum of specificity plus sensitivity. For contexts in which higher sensitivity or specificity is desired, other cut-offs may be preferred.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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 | 0.000 |
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".