Use of emergency departments and primary care visits for asthma related conditions in the 3 years following an asthma education program
Bibliographic record
Abstract
BACKGROUND: This study examines changes in Primary Care Visits (PCVs) and Emergency Department Visits (EDVs) among 1918 patients with asthma who attended either two visits, one visit or were no-show referrals at the Dr. Patrick Gill Asthma Education Center (AEC) in Charlottetown Prince Edward Island (PEI) between January 1, 2003 and March 31, 2008 compared to 2799 controls selected from a list of PEI asthma patients developed for the Canadian Chronic Disease Surveillance System (CCDSS). METHODS: Hurdle regression was used to model counts of PCVs and negative binomial models were used to model counts of EDVs at 12 months prior to AEC contact and 0-1, >1 to 2 and >2 to 3 years after AEC contact. The PEI Research Board approved the project. RESULTS: No-show referrals had a significant increase in pediatric EDVs and PCVs in the first year after referral. The higher rates of PCVs and EDVs prior to contact with the AEC in patients referred to the AEC were reduced after contact with the AEC, although they remained significantly higher than the CCDSS controls. CONCLUSIONS: Compared to patients who attended the AEC, referred patients who did not attend the AEC did not achieve similar reductions in pediatric EDVs and PCVs in the first year after referral.
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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 | 0.003 |
| 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 | 0.000 |
| Open science | 0.001 | 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".