Profiles of Frequent Geriatric Users of Emergency Departments: A Latent Class Analysis
Bibliographic record
Abstract
BACKGROUND/OBJECTIVES: Frequent geriatric users of emergency departments (EDs) represent a complex and heterogeneous population. Identifying their specific subgroups would allow the development of interventions better customized to their needs and characteristics. Thus, this study aimed to develop profiles of frequent geriatric ED users using the individual characteristics of patients. DESIGN: This was a retrospective cohort study. SETTING: Databases from the Régie de l'assurance maladie du Québec (RAMQ) were utilized. PARTICIPANTSThis study included individuals aged 65 years or older living in the community in the Province of Quebec (Canada), who consulted in an ED at least four times in the year after an ED index date (an ED visit, chosen randomly, during an index period of January 1, 2012 to December 31, 2013) and who had received a diagnosis of ambulatory care-sensitive conditions (ACSCs) in the 2 years preceding the index date. MEASUREMENTS: A latent class analysis was used to identify subgroups of frequent geriatric ED users according to their individual characteristics, including ACSC type, dementia, mental health disorders, cancer diagnosis, and comorbidity index. RESULTS: The study cohort consisted of 21,393 frequent geriatric ED users. Four groups of frequent geriatric ED users were identified: people with low comorbidity (39.0%), comprising the individuals with the lowest number of physical and mental health conditions; people with cancer (32.7%); people with pulmonaryand cardiac diseases (18.1%); and people with dementia or mental health disorders (10.2%), composed of individuals with the highest proportion of common and severe mental health disease, as well as dementia. This group accounts for the highest use of overall healthcare services. CONCLUSION: These profiles will be useful in developing customized interventions addressing the needs of each subgroup of frequent geriatric ED users. More research is needed to bridge the remaining gaps, especially regarding the healthiest frequent geriatric users of EDs.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".