Commonly Used Screening Instruments to Identify Frailty Among Community-Dwelling Older People in a General Practice (Primary Care) Setting: A Study of Diagnostic Test Accuracy
Bibliographic record
Abstract
BACKGROUND: Rapid frailty screening remains problematic in primary care. The diagnostic test accuracy (DTA) of several screening instruments has not been sufficiently established. We evaluated the DTA of several screening instruments against two reference standards: Fried's Frailty Phenotype [FP] and the Adelaide Frailty Index [AFI]), a self-reported questionnaire. METHODS: DTA study within three general practices in South Australia. We randomly recruited 243 general practice patients aged 75+ years. Eligible participants were 75+ years, proficient in English and community-dwelling. We excluded those who were receiving palliative care, hospitalized or living in a residential care facility.We calculated sensitivity, specificity, predictive values, likelihood ratios, Youden Index and area under the curve (AUC) for: Edmonton Frail Scale [EFS], FRAIL Scale Questionnaire [FQ], Gait Speed Test [GST], Groningen Frailty Indicator [GFI], Kihon Checklist [KC], Polypharmacy [POLY], PRISMA-7 [P7], Reported Edmonton Frail Scale [REFS], Self-Rated Health [SRH] and Timed Up and Go [TUG]) against FP [3+ criteria] and AFI [>0.21]. RESULTS: We obtained valid data for 228 participants, with missing scores for index tests multiply imputed. Frailty prevalence was 17.5% frail, 56.6% prefrail [FP], and 48.7% frail, 29.0% prefrail [AFI]. Of the index tests KC (Se: 85.0% [70.2-94.3]; Sp: 73.4% [66.5-79.6]) and REFS (Se: 87.5% [73.2-95.8]; Sp: 75.5% [68.8-81.5]), both against FP, showed sufficient diagnostic accuracy according to our prespecified criteria. CONCLUSIONS: Two screening instruments-the KC and REFS, show the most promise for wider implementation within general practice, enabling a personalized approach to care for older people with frailty.
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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 | 0.028 | 0.083 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 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".