Emergency department increased use of observation care for elderly medicare patients
Bibliographic record
Abstract
BACKGROUND: Over the past decade, a growing number of older Medicare beneficiaries visit the Emergency Department (ED) and have been placed in observation care. We investigated and compared the prevalence and factors associated with patients age ≥ 65 years with Medicare insurance who are placed in the hospital, observation care, or discharged following an ED visit. METHODS: We conducted a retrospective cohort study using data from a nationally representative 5% sample of Medicare patients age ≥ 65 years during the year 2013. We performed multiple generalized estimating equation (GEE) logistic regression analyses to assess the relationship between placement in a hospital vs. discharge, observation care vs. discharge, and observation care vs. admission. RESULTS: Of 537,455 Medicare beneficiaries age ≥ 65 years who visited an ED in 2013, 48.0% (N= 258,083) were discharged, 10.5% (N=56,184) placed in observation care, and 41.5% (N=223,188) were admitted to the inpatient service following the ED visit. The top 2 diagnoses associated with placement in the hospital vs. discharge were ischemic heart disease and renal disease. Patients with symptomatic diagnoses such as chest pain and dizziness were more likely to be placed in observation care following an ED visit as compared to admission to the hospital. CONCLUSION: Compared to prior studies, we found a greater number of older Medicare ED patients placed in observation care and a lower number admitted to the hospital. Most common diagnoses of placement in observation care were symptom-based as compared to being admitted to the hospital which were disease-based.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".