Tablet-Based Frailty Assessments in Emergency Care for Older Adults
Bibliographic record
Abstract
The rise in aging populations worldwide places a focus on shifting healthcare needs to match changing demographics. Older adults have a higher risk of becoming frail and losing functional abilities (e.g., walking and bathing) as well as the ability to perform daily activities such as shopping and cooking. Providing care and appropriate interventions to assist older adults with frailty is contingent upon identifying these individuals through effective screening. Frailty is often characterized by self-reports of exhaustion, weakness, slowing, and low physical activity. Older adults at risk of becoming frail often enter the healthcare system through emergency services (e.g., calling 911 or presenting at an emergency department), and screening should target these entry points. This paper discusses the design process, and usability findings associated with a tablet-based battery of frailty measures for assessing functional and cognitive states in elderly adults while being admitted to emergency care. This research is focusing on the use of digital technologies as a medium for physical and mental frailty assessment in emergency care. A diverse group of healthcare users is envisaged including paramedics, physicians, and research personnel, as well as end-users such as elderly patients and their caregivers. We describe the development and usability of the tablet-based frailty assessment system and we report on the concurrent validity of frailty measures.
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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 | 0.002 | 0.011 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".