Characteristics of frequent users of emergency departments living with major neurocognitive disorders: a cohort study
Bibliographic record
Abstract
Objective: This study aimed to describe and compare the characteristics of community-dwelling older adults living with or without major neurocognitive disorders who made frequent use of emergency departments. Methods: This is a retrospective cohort study based on a secondary analysis of provincial health administrative data in Quebec, Canada. We included community-dwelling older adults from Quebec who were considered frequent emergency department users (a minimum of 4 visits in the year following an index emergency department visit chosen randomly between January 1, 2012, and December 31, 2013) and who had been diagnosed with at least one chronic condition. We compared characteristics of frequent users living with or without major neurocognitive disorders using chi-square and Kruskal-Wallis tests. Results: The study cohort consisted of 21 393 frequent emergency department users, of which 3051 (14.26%) were identified as having a major neurocognitive disorder. The results highlight a higher burden of chronic conditions, polypharmacy, antipsychotic use, and past use of healthcare services among these individuals. The results also reveal a higher proportion of conditions associated with geriatric syndromes such as trauma and injury, malnutrition, orthostatic hypertension, and gait disorders. Conclusion: Frequent emergency department users living with major neurocognitive disorders represent a complex population. Our results highlight the importance of systematically addressing their needs in appropriate settings and through customized interventions.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".