Adult Asthma Diagnosis: Physician Reported Challenges in Alberta-Based Primary Care Practices
Bibliographic record
Abstract
INTRODUCTION: An estimated 8.1% of Canadians adults have asthma. While there are challenges associated with the use of objective measurement of lung function in the diagnosis of asthma, we are uncertain of the barriers that impact the use of objective measures, and have limited understanding of the challenges experienced by primary care providers in diagnosis of asthma. The objectives of this quality improvement initiative were to identify primary care providers' methods of diagnosing asthma and to identify challenges with diagnosis. METHODS: An online survey was disseminated using a snowball methodology. SETTING: Primary care practices in Alberta, Canada. PARTICIPANTS: A total of 84 primary care providers completed the survey. MAIN OUTCOME MEASURES: methods for diagnosing asthma and to identify challenges in their practice related to asthma diagnosis. RESULTS: They identified full pulmonary function testing (54%), pre- and postbronchodilator spirometry (54%), complete history and physical (42%), peak flow measurement overtime (26%), pulmonary consult (26%), and trial of asthma medication(s) (23%), as ideal methods of diagnosing asthma. The most significant barriers to diagnosis included episodic care-care provided typically during times of worsening symptoms without ongoing preventative/maintenance care (55%), patient follow-up (44%), conflict between clinical impression and pulmonary function results (43%), patient already on asthma medications (43%), and interpreting spirometry/pulmonary function results (39%). CONCLUSION: The results of this survey indicate that the majority of primary care providers would choose full pulmonary function testing or pre- and postbronchodilator spirometry as the ideal methods of diagnosing asthma. However, barriers related to the nature of asthma care, patient factors, and challenges with diagnostic testing create challenges. This study also highlights that primary care providers have adapted to challenges in leveraging objective measurement and may rely upon other methods for diagnosis such as trials of medications.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| 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".