[Management and orientation of geriatric patients admitted to emergencies for a fall: results of the French prospective OREGoN cohort study].
Bibliographic record
Abstract
Falls in older adults are a frequent reason for admission to the emergency department, associated with greater morbidity and mortality risks, and justify specialized geriatric expertise. Our objective was to determine i) the number of older fallers admitted to the emergency department for a serious fall, and ii) the proportion of those who were referred to a geriatrician in the following 12 months. METHODS: We included all patients aged 75 and over admitted to the emergency department of the University hospital of Angers, France, for a fall between 1st October and 1st November 2015. The consensual criteria proposed by the French national authority for health (2009) were used to define serious falls. RESULTS: Of the 214 older fallers admitted to the emergency department, 213 (99.5%) had at least one severity criterion for the fall. Only 40 older patients (18.7%) were referred to a geriatrician during the following 12 months. They exhibited more frequently a post-fall syndrome (p=0.007), more than 3 fall risk factors (p <0.001), and took more often an anticoagulant (p=0.032) than those who had not been referred to a geriatrician. CONCLUSIONS: Although almost all older fallers admitted to the emergency room had experienced a serious fall, only a minority of them received a geriatric assessment in the following year.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".