Agreement between self-reported asthma symptoms and exhaled nitric oxide levels: impact on inhaled corticosteroid prescribing in general practice. An observational study
Bibliographic record
Abstract
BACKGROUND: The National Review of Asthma Deaths UK highlighted that 46% of deaths could be avoided and recommended that all sufferers receive a structured asthma annual review which assess asthma control. In primary care this is commonly achieved using symptom-based questionnaires such as the Asthma Control Test (ACT). A newer method of assessing asthma control is Fractional Exhaled Nitric Oxide (FeNO) testing, which is currently recommended for the diagnosis of asthma, but not for monitoring of asthma control. The study aim was to assess the correlation between self-reported symptoms as measured by the ACT and FeNO testing and the subsequent impact of FeNO testing on prescribing of asthma medication. METHODS: test was used to compare prompting frequency of the measures and Kendalls τ statistic was made to estimate their concordance and influence on subsequent ICS medication prescription. RESULTS: The mean age of the participants was 41 years (4-93 years). There was no statistically significant correlation between ACT and FeNO (ρ = 0.195, p = 0.120). The median FeNO was 26 ppb (range 8-279 ppb), and the ACT score 20 (range 5 to 25 points). Furthermore, FeNO more frequently prompts a change in medication than ACT, 66% versus 42% (p = 0.005). A low concordance between the measures was found (Kendall's τ statistic - 0.321). CONCLUSION: FeNO should be considered for monitoring of control in asthma. To balance the cost of implementing this technology into primary care a risk stratified approach could be applied to testing.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".