A Randomized Clinical Trial to Assess the Impact on an Emergency Response System on Anxiety and Health Care Use among Older Emergency Patients after a Fall
Bibliographic record
Abstract
Objectives: Personal emergency response systems (PERSs) are reported to reduce anxiety and health care use and may assist in planning the disposition of older patients discharged from the emergency department (ED) to home. This study measured the impact of a PERS on anxiety, fear of falling, and subsequent health care use among older ED patients. Methods: This study was a randomized controlled trial comparing PERS use with standard ED discharge planning in subjects 70 years of age or older discharged home after a fall. Outcome assessors were blinded to the study objectives. Anxiety and fear of falling were measured at baseline and 30 days using the Hospital Anxiety and Depression Scale anxiety subscale (HADS-A) and modified Falls Efficacy Scale (mFES). Return to the ED, hospitalization, and length of stay were recorded after 30 and 60 days. Results: Eighty-six subjects were randomized and completed follow up (43 per group). There was no important difference in mean reduction in anxiety (mean change treatment − control, +0.35; 95% confidence interval [CI] =−1.5 to 0.76; p = 0.55) or fear of falling (mean change, +4.5; 95% CI =−6.7 to 15.7; p = 0.70). Return visits to the ED occurred in eight of 43 patients in both the control and treatment groups (risk difference, 0.0%; 95% CI =−16% to 16%). Hospitalization occurred in six of 43 in the control group versus three of 43 in the treatment group (risk difference treatment − control =−7.0%; 95% CI =−19.8% to 5.9%). Conclusions: In contrast to previous studies, there was no evidence that a PERS reduced anxiety, fear of falling, or return to the ED among older persons discharged from the ED.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".