Suboptimal asthma control: prevalence, detection and consequences in general practice
Bibliographic record
Abstract
Telephone surveys describing suboptimal asthma control may be biased by low response rates. In order to obtain an unbiased assessment of asthma control and assess its impact in primary care, primary care physicians used a 1-page control questionnaire in 50 consecutive asthma patients. Of the 10,428 patients assessed by 354 physicians, 59% were uncontrolled, 19% well-controlled and 23% totally controlled. Physicians overestimated control, regarding only 42% of patients as uncontrolled. Physicians were more likely to report plans to alter the regimens of uncontrolled patients than controlled patients (1.29 versus 0.20 medication changes per patient) doing so in a fashion consistent with guideline recommendations. Of the uncontrolled patients, 59% required one or more urgent care or specialist visits versus 26 and 15% of well-controlled or totally controlled patients, respectively. Patients were more likely to report short-term symptom control when they had not required urgent or specialist care (odds ratio 5.68; 95% confidence interval 4.91-6.58). The majority of asthma patients treated in general practice are uncontrolled. Lack of control can be recognised by physicians who are likely to consider appropriate changes to therapy. A lack of short-term symptom control of asthma is associated with excess healthcare utilisation.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".