[Loyal frequent users of hospital emergency departments: the FIDUR project].
Bibliographic record
Abstract
OBJECTIVES: To describe the characteristics of frequent users of hospital emergency departments and analyze whether characteristics varied in relation to how revisits were distributed over the course of the year studied. MATERIAL AND METHODS: Retrospective study of patients over the age of 14 years who were treated in a hospital emergency department at least 10 times in 2013. Patients were identified in 17 public hospitals in the Spanish autonomous community of Madrid. Data related to the first and successive visits were gathered and analyzed by quarter year. RESULTS: We included 2340 patients with a mean (SD) age of 54 (21) years. A total of 1361 (58.%) were women, 1160 (50%) had no concomitant diseases, 1366 (58.2%) were substance abusers, and 25 (1.1%) were homeless. During the first visit, 2038 (87.1%) complained of a recent health problem, and 289 (12.4%) were admitted. Sixty (2.6%) patients concentrated their revisits in a single quarters 335 (14.3%) in 2 quarters, 914 (39.1%) in 3, and 1005 (42.9%) in 4. Patients whose revisits were distributed over more quarters were older (> 65 years), had more concomitant conditions, were on more medications (P < .001), showed cognitive impairment (P = .039), and were more functionally dependent (P = .007). They were also more likely to have been hospitalized on the first visit (P < .001). Patients whose revisits were concentrated in fewer quarters were more often women (P = .012) and more likely to have a specific diagnosis (P < .001) and revisit for a reason related to the initial visit (P = .012). CONCLUSION: Our study shows that the frequent user has specific characteristics and loyally comes to the same emergency department over the course of a year. Patients whose revisits are dispersed over a longer period have more complex problems and use more resources during their initial visit.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".