Healthcare outcomes and dispositions in persons with obesity within emergency departments in Ontario, Canada: A cross-sectional analysis of the National Ambulatory Care Reporting System (NACRS), 2018–2022
Bibliographic record
Abstract
INTRODUCTION: The experience of persons with obesity (PwO) in the Canadian healthcare setting has not been widely studied. The objective of this study was to assess care in PwO in emergency departments in Ontario, Canada. METHODS: This secondary analysis made use of 2018-2022 Canadian Institute for Health Information's National Ambulatory Care Reporting System. The sample consisted of 4547 individuals with an obesity diagnosis, and 4547 controls who were matched for sex, age, and main diagnosis. Ordinal logistic and multiple linear regression analyses were used to assess triage scores, wait times, and length of stay. RESULTS: PwO had 4.8 minutes longer wait time for a physician initial assessment (p<0.01), 3.56 hours longer length of stay in the emergency department (p<0.0001), and 55% greater odds (OR = 1.55, 95% CI: 1.43-1.68) of having a less urgent triage score compared to controls matched for main diagnosis. When further matched for triage score, PwO experienced over three hours longer length of stay for triage level 2 (emergent, p<0.01), five hours longer for triage level 3 (urgent, p<0.01), and nearly two hours longer for triage level 4 (less urgent, p<0.05) cases. CONCLUSION: PwO were rated as less urgent and experienced longer wait times and length of stay, compared to controls matched by sex, age, and main diagnosis. Additional research is needed to confirm the consistency of these findings in other provinces/territories, and to examine clinical outcomes, and the underlying reasons for differences.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".