Fall Prevalence and Associated Risk Factors in the Hospitalised Adult Population: A Crucial Step Towards Improved Hospital Care
Bibliographic record
Abstract
Background Falls among the adult population are a major global health concern with severe repercussions for individuals and healthcare systems. The purpose of this study was to investigate the prevalence and associated risk factors of falls in hospitalized patients in order to improve hospital care for elderly adults. Materials and methods The research was conducted at two institutions of tertiary care in Abbottabad, Pakistan. After extensive screening and obtaining informed consent, a total of 210 participants aged 50 and older were enrolled in the study. Mental status, history of falls, ambulation/elimination status, vision, gait/balance, systolic blood pressure, medication use, and predisposing diseases were evaluated using the Long Term Care Fall Risk Assessment Form. Additionally, the Dynamic Gait Index was utilized to evaluate various aspects of gait. Results 58.6% of participants reported a history of falls in the previous year, according to the findings. BMI, imbalance, vertigo, and fear of falling were significantly associated with an increased risk of falls in older individuals. The Long-Term Care Fall Risk Assessment, the Montreal Cognitive Assessment (MoCA), the Dynamic Gait Index (DGI), and the Mini-BESTest scores revealed that patients with a history of falls had inferior functional and cognitive outcomes. Falls were more common among individuals with a robust BMI, especially men. Conclusions The study results highlight the multifactorial nature of falls in the adult population and the need for targeted interventions to address modifiable risk factors. To enhance hospital care for high-risk patients, proactive fall prevention strategies, including regular risk assessments and individualized interventions, should be implemented. This study provides important insights into the prevalence and causes of accidents among hospitalized patients, particularly in developing nations such as Pakistan. .
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".