Risk factors for frequent users of the emergency department among adults aged 55 and older
Bibliographic record
Abstract
Introduction: Excessive use of the emergency department (ED) is a major source of healthcare expenditure. ED frequent users, have been identified as a major contributing factor to a disporportionate amount of ED visits and costs, making up 20% to 30% of all annual visits. The aim of the study was to identify risk factors that place adults age 55 and older at risk for frequent ED use.Methods: The Transitional Care Model (TCM): Hospital Discharge Screening Criteria for High Risk Older Adults was used to identify risk factors for frequent use of ED services in adults 55 and older.Results and conclusions: A third of the sample (33%) had active behavioral and/or psychiatric issues. A majority of the sample (87%) had two or more hospitalizations within 6 months of a prior ED visit, and seventy-two percent were hospitalized within thirty days of an Emergency Department visit. Almost 70% had at least 1 chronic diagnosis of diabetes (41.5%), heart failure (35.8%), or COPD (28%). Most patients were between ages 70-85 years old and risk factors for ED frequent use included 4 or more coexisting health conditions, 6 or more prescription medications, previous hospital admissions, active behavioral and/or psychiatric issues. Identifying older adults at high risk for ED frequent use may provide earlier interventions and less reliance on ED use for care and treatment of chronic disorders.
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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 | 0.000 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".