Identifying frailty in primary care: A systematic review
Bibliographic record
Abstract
AIM: Identification of frailty in the primary care setting could be improved with the availability of easily identifiable markers of frailty. The purpose of this article was to systematically review markers for frailty or risk tools that have been validated in the ambulatory care setting. METHODS: Medline, PubMed, CIHAHL and Embase databases were searched up to March 2016 for studies on frailty markers in community-dwelling individuals 65 years or older. Studies were included for review if they were carried out in primary care or outpatient settings, used a validated definition of frailty, compared two or more markers, and used randomized controlled trial, quasi-experimental or prospective cohort designs. RESULTS: Of the 3405 titles screened, 12 were retained for review. All of the studies were prospective cohort designs. Studies most frequently assessed biological markers, such as immune, inflammation, endocrine biomarkers and metabolic syndrome markers. Not one specific marker was repeatedly identified as a definitive marker for frailty. CONCLUSIONS: There is a lack of psychometrically sound and clinically useful frailty markers. There is a need for further research to identify highly sensitive, specific and accurate markers that are feasible to use in the context of busy primary care practice. Geriatr Gerontol Int 2017; 17: 1358-1377.
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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 | 0.004 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.004 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".