UTILIZING VALID, RELIABLE, AND PRACTICAL MEASURES OF HEALTH STATUS IN PRIMARY GERIATRIC CARE: TRANSLATING RESEARCH INTO USUAL CARE WITH THE SENIOR’S HEALTH ASSESSMENT REPORT AND PLAN (SHARP™)
Bibliographic record
Abstract
Abstract Introduction To better support evidenced-based care, we combined 9 valid, reliable, and clinically useful tests into a single health status and risk assessment tool, performed by a Team nurse, called the Seniors Health Assessment, Report and Plan (SHARP™). These tests include: CFS, EQ5D-5L, EQ-VAS, MoCA, GDS, Months of the Year Backwards, Gait Speed, Grip Strength, Water Swallow Test, and MNA-6. A published study, with 18 months of follow-up for a primary geriatric home-based practice demonstrated that these tests were stronger predictors of death, nursing home transfer (NHT), or hospital admission(HA) compared to any medical diagnosis, multiple comorbidities, or polypharmacy. Hazard Ratios for SHARP™ vs medical diagnoses were: Death (median HR 5.9 vs. 1.6), NHT (median HR 4.6 vs.1.4), and HA (median HR 6.0 vs. 1.6). The research presented here will provide several case-based studies to demonstrate how we have translated this research into “usual care” for a primary home-based interdisciplinary geriatric medical practice. Specifically, we will demonstrate how we: 1. share this data with patients and caregivers to motivate them for Team interventions, 2. share a summary report of an individual’s health with the hospital and other community care providers, 3. use these tests to guide and evaluate Team interventions for individual patients (e.g. changes in gait speed), 4. Track changes in health status and risk, and 5. use aggregated data at a program level for benchmarking, defining population needs, and for planning and evaluation. Conclusions Standardized testing is acceptable to patients, efficient, supports evidenced-based care, and is useful for program planning and evaluation.
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 | 0.060 | 0.110 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".