Asthma program evaluation: Impact of tertiary Asthma Care Network on ED visits and hospitalizations
Bibliographic record
Abstract
Rationale: Program evaluation is often hampered by lack of comparative control data. We aimed to determine the impact of an interdisciplinary tertiary Asthma Care (ACN) on acute health services utilization (HSU). Methods: Data from ACN patients seen between Jan 1, 2009 and Dec 31, 2018 were linked to Ontario’s administrative databases at the Institute for Clinical Evaluative Sciences (ICES). Control subjects matched for age, sex and year of asthma diagnosis were identified from the ICES asthma cohort. We assessed the odds of ED visits and hospitalizations for asthma between cases and controls, adjusting for acute HSU in the 12 months preceding the index visit. Results: Health records from 1,248 ACN patients (age 33.2 ± 25.0 [mean±SD] years, 57% female) were matched 1:3 to 3,629 Controls (age 32.7 ± 24.9, years, 57% female). ORs are shown in Table 1. ED visits and hospitalizations were reduced for 21% and 10.7% of ACN patients respectively, compared to 6.7% and 1.4% of Controls respectively (both P<0.001). Conclusions: Compared to control subjects identified from health administrative data, HSU is higher in patients seen in a tertiary ACN, and those with a history of previous ED visits and comorbidities. ACN patients experienced greater improvements in HSU in the 2 years following their index visit. Linking clinical and administrative data lends rigour to program evaluation and will inform quality improvement
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".