How accurate is the diagnosis of exercise induced asthma among Vancouver schoolchildren?
Bibliographic record
Abstract
BACKGROUND: Limited access to exercise testing facilities means that the diagnosis of exercise induced asthma (EIA) is mainly based on self-reported respiratory symptoms. This is open to error since the correlation between exercise related symptoms and subsequent exercise testing has been shown to be poor. AIM: To study the accuracy of clinically diagnosed EIA among Vancouver schoolchildren. METHODS: Fifty two children referred for investigation of poorly controlled EIA were studied. Following a careful history and physical examination, children performed pulmonary function tests before, then 5 and 15 minutes after a standardised treadmill exercise test. Based on overall assessment, a diagnostic explanation for each child's respiratory complaints was provided as far as possible. RESULTS: Only eight children (15.4%) fulfilled diagnostic criteria for EIA (fall in FEV(1) > or =10%). Of the remainder: 12 (23.1%) were unfit, 14 (26.9%) had vocal cord dysfunction/sigh dyspnoea, 7 (13.5%) had a habit cough, and 11 (21.1%) had no abnormalities on clinical or laboratory testing, so were given no diagnosis. Initial reported symptoms of wheeze or cough often changed significantly following a careful history, particularly among the eight elite athletes. The final complaint was sometimes not respiratory, and, in a few cases, was not even associated with exercise. CONCLUSIONS: The clinical diagnosis of EIA is inaccurate among Vancouver schoolchildren, principally due to the unreliability of their initial exercise related complaints. Symptom exaggeration, familiarity with medical jargon, and psychogenic complaints are all common. A careful history is essential in this population before basing any diagnosis on self-reported respiratory symptoms.
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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 | 0.002 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".