Implementation of the Integrated Care of Older People (ICOPE) App and ICOPE Monitor in Primary Care: A Study Protocol
Bibliographic record
Abstract
INTRODUCTION: The World Health Organization (WHO) has recently launched the term "intrinsic capacity", defined as "the composite of all the physical and mental capacities of an individual". Intrinsic capacity has a positive value towards healthy aging, and is constructed by five domains: cognition, vitality/nutrition, sensory, psychology, and mobility. ICOPE App and ICOPE Monitor are applications for the assessment (screening) of intrinsic capacity. HYPOTHESIS: Intrinsic capacity assessed by the ICOPE Apps at baseline could be associated with the incidence of frailty, functional decline, and health outcomes during 1-year follow-up. OBJECTIVES: To assess the association between intrinsic capacity measured by the ICOPE Apps at baseline and the incidence of frailty in community-dwelling older adults during 1-year follow-up. Secondarily, to assess the association of intrinsic capacity and functional decline, mortality, pre-frailty, falls, institutionalization, and quality of life. METHODS: Protocol for a cohort study of community-dwelling adults ≥65-year-old, with no other exclusion criteria than the inability to use the Apps or communicate by telephone/video-call for any reason (cognitive or limited access to telephone/video-call) OR being considered frail at baseline (defined as having a Rockwood's clinical frailty scale, CFS score ≥4). Intrinsic capacity measured by the ICOPE Apps and CFS will be assessed at baseline, 4-, 8- and 12-month follow-up by telephone/video-call. Assuming a prevalence of frailty of 10.7%, and incidence of 13% (alpha-risk=0.05), 400 participants at 12-month end-point (relative precision=0.10) and 600 participants at baseline will be required. RESULTS: Associations among the decrease in intrinsic capacity and higher risk of frailty, functional decline, and health adverse outcomes during 1-year follow-up are expected. CONCLUSIONS: ICOPE Apps might identify individuals at higher risk of frailty, functional decline, and health adverse outcomes. The implementation of the ICOPE Apps into clinical practice might help to deliver efficient person-centered care-plans, and benefit the healthcare systems.
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 | 0.027 | 0.015 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.044 | 0.012 |
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".