Frailty Screening in Low‐ and Middle‐Income Countries: A Systematic Review
Bibliographic record
Abstract
OBJECTIVES: To conduct a systematic review of frailty screening tools used in low- and middle-income countries (LMICs). DESIGN: Systematic review. SETTING: LMICs, as defined by the World Bank on June 30, 2014. PARTICIPANTS: Elderly adults (as defined by the authors) living in LMICs. MEASUREMENTS: Studies were included if the population under consideration lived in a LMIC, the study involved an assessment of frailty, the study population was elderly adults, and the full text of the study was available in English. The Medline, Embase, CINAHL and PsychINFO databases were searched up to June 30, 2014. RESULTS: Seventy studies with data from 22 LMICs were included in the review. Brazil, Mexico, and China provided data for 60 of the 70 studies (85.7%), and 15 countries contributed data to only one study. Thirty-six studies used the Fried criteria to assess frailty, 20 used a Frailty Index, and eight used the Edmonton Frailty Scale; none of the assessment tools used had been fully validated for use in a LMIC. CONCLUSION: There has been a rapid increase in the number of published studies of frailty in LMICs over the last 5 years. Further validation of the assessment tools used to identify frail elderly people in LMICs is needed if they are to be efficient in identifying those most in need of health care in such settings.
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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 | 0.012 | 0.050 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".