Qualitative study of practices and challenges when making a diagnosis of asthma in primary care
Bibliographic record
Abstract
Misdiagnosis (over-diagnosis and under-diagnosis) of asthma is common. Under-diagnosis can lead to avoidable morbidity and mortality, while over-diagnosis exposes patients to unnecessary side effects of treatment(s) and results in unnecessary healthcare expenditure. We explored diagnostic approaches and challenges faced by general practitioners (GPs) and practice nurses when making a diagnosis of asthma. Fifteen healthcare professionals (10 GPs and 5 nurses) of both sexes, different ages and varying years of experience who worked in NHS Lothian, Scotland were interviewed using in-depth, semi-structured qualitative interviews. Transcripts were analysed using a thematic approach. Clinical judgement of the probability of asthma was fundamental in the diagnostic process. Participants used heuristic approaches to assess the clinical probability of asthma and then decide what tests to do, selecting peak expiratory flow measurements, spirometry and/or a trial of treatment as appropriate for each patient. Challenges in the diagnostic process included time pressures, the variable nature of asthma, overlapping clinical features of asthma with other conditions such as respiratory viral illnesses in children and chronic obstructive pulmonary disease (COPD) in adults. To improve diagnostic decision-making, participants suggested regular educational opportunities and better diagnostic tools. In the future, standardising the clinical assessment made by healthcare practitioners should be supported by improved access to diagnostic services for additional investigation(s) and clarification of diagnostic uncertainty.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".