Can Access to Spirometry in Asthma Education Centres Influence the Referral Rate by Primary Physicians for Education?
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Asthma remains uncontrolled in a large number of asthmatic patients. Recent surveys have shown that a minority of asthmatic patients are referred to asthma educators. The objective of the present study was to assess the influence of increased access to spirometry in asthma education centres (AECs) on the rate of patient referrals to these centres by general practitioners. METHODS: A one-year, prospective, randomized, multicentric, parallel group study was conducted over two consecutive periods of six months each, with added spirometry being offered in the second six-month period to the experimental group. Ten AECs were enrolled in the project. An advertisement describing the AECs' services was sent by mail to a total of 303 general practitioners at the start of each period, inviting them to refer their patients. Measures of the frequency of medical referrals to the AECs were assessed for each period. RESULTS: The group of AECs randomly selected for spirometry in the second six-month period received 48 medical referrals during the first period and 32 during the second one, following proposed spirometry. AECs that had not offered spirometry received five referrals during the first period and seven during the second period. One AEC withdrew a few weeks after the study began and others encountered administrative problems, reducing their ability to provide interventions. CONCLUSIONS: Referral to AECs is not yet integrated into the primary care of asthma and offering more rapid access to spirometry in the AECs does not seem to be a significant incentive for such referrals.
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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 | 0.008 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.003 | 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".