A real-world evaluation of the effectiveness and Sufficiency of Current Emergency Department Preventative Strategies for Reducing Emergency Department revisits in a Canadian children’s hospital: a retrospective cohort study
Bibliographic record
Abstract
BACKGROUND: Despite asthma guidelines' recommended emergency department preventative strategies (EDPS), repeat asthma-related emergency department (ED) visits remain frequent. METHODS: We performed a retrospective cohort study of children aged 1-17 years presenting with asthma to the Children's Hospital of Eastern Ontario (CHEO) ED between September 1, 2014 - August 31, 2015. EDPS was defined as provision of education on trigger avoidance and medication technique plus documentation of an asthma action plan, a prescription for an inhaled controller medication or referral to a specialist. Logistic regression was used to identify factors associated with receipt of EDPS. We further compared the odds of repeat presentation to the ED within the following year among children who had received EDPS versus those who had not. RESULTS: 1301 patients were included, and the mean age of those who received EDPS was 5.0 years (SD = 3.7). Those with a moderate (OR = 3.67, 95% CI: 2.49, 5.52) to severe (OR = 3.69, 95% CI: 2.50, 5.45) asthma presentation were most likely to receive EDPS. Receiving EDPS did not significantly reduce the adjusted odds of repeat ED visits, (OR = 0.82, 95% CI: 0.56, 1.18, p = 0.28). CONCLUSIONS: Patients with higher severity asthma presentations to the ED were more likely to receive EDPS, but this did not appear to significantly decrease the proportion with a repeat asthma ED visit. These findings suggest that receipt of EDPS in the ED may not be sufficient to prevent repeat asthma ED visits in all children.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 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".