Risky Business: Factors That Increase Risk of Falls Among Older Adult In-Patients
Bibliographic record
Abstract
In hospitals, older patients are at increased risk of falling multiple times. This study incorporated an epidemiologic cross-sectional design consisting of 4,348 older patients (≥65-year-old). Eight hundred eighty five (20.4%) in-patients experienced multiple falls while remaining participants had one fall incident. A patient fall event was recorded with age, sex, incident date, type of fall, and location. Logistic regression assessed risk factors found in patients with multiple falls compared to those with one fall. Significant differences were observed in the proportion of multiple falls: in a bed with no rails, standing, walking, and using a wheel/Geri chair ( p < .05). Overall, sex, type of fall, and location were significant in predicting multiple falls ( p < .05). Male patients were at 16.1% greater risk of multiple falls, when compared to females ( p < .05). A fall in complex care, mental health, or respirology were more likely to experience multiple falls ( OR = 2.659, 3.620, 1.593 respectively), while season had no impact.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".