The Management of Asthma: A Case-Scenario-Based Survey of Family Physicians and Pulmonary Specialists
Bibliographic record
Abstract
This study assessed family physicians' and pulmonary specialists' approaches to the treatment of adult outpatient asthma using a self-administered questionnaire consisting of six asthma scenarios of varying severity levels. One hundred sixty-three randomly selected family physicians and pulmonary specialists completed the questionnaire (response rate of 80%). We observed that, regardless of asthma severity, more than 75% of physicians (regardless of specialty) would not include oral theophylline or nonsteroidal anti-inflammatory preparations in their treatment approach. Pulmonary specialists' and family physicians' approaches to mild asthma were similar (more than 90% recommended an inhaled beta2-agonist). However, considerable differences existed among and between physician groups for the remaining scenarios. For example, with an exacerbation associated with an upper respiratory tract infection, family physicians were more likely to recommend oral antibiotics (p<0.0001) and a same-day outpatient visit (p<0.0001), whereas specialists were more likely to increase the inhaled corticosteroid dosage (p<0.0001). Overall, disagreement was observed almost twice as often among family physicians than among specialists. Our results suggest that physicians vary markedly in their reported use of most interventions available to treat asthma, even when the disease severity is specified.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| 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".