Frailty Screening and Case-Finding for Complex Chronic Conditions in Older Adults in Primary Care
Bibliographic record
Abstract
With the aging population, escalating demand for seniors' care and limited specialist resources, new care delivery models are needed to improve capacity for primary health care for older adults. This paper describes the "C5-75" (Case-finding for Complex Chronic Conditions in Seniors 75+) program, an innovative care model aimed at identifying frailty and commonly associated geriatric conditions among older adults within a Canadian family practice setting and targeting interventions for identified conditions using a feasible, systematic, evidence-informed multi-disciplinary approach. We screen annually for frailty using gait speed and handgrip strength, screen for previously undiagnosed comorbid conditions, and offer frail older adults multi-faceted interventions that identify and address unrecognized medical and psychosocial needs. To date, we have assessed 965 older adults through this program; 14% were identified as frail based on gait speed alone, and 5% identified as frail based on gait speed with grip strength. The C5-75 program aims to re-conceptualize care from reactive interventions post-diagnosis for single disease states to a more proactive approach aimed at identifying older adults who are at highest risk of poor health outcomes, case-finding for unrecognized co-existing conditions, and targeting interventions to maintain health and well-being and potentially reduce vulnerability and health destabilization.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".