Understanding Frailty Screening: a Domain Mapping Exercise
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Background Many definitions and operationalisations of frailty exclude psychosocial factors, such as social isolation and mental health, despite considerable evidence of the links between frailty and these factors. This study aimed to investigate the health domains covered by frailty screening tools. Methods A systematic search of the literature was conducted in accordance with PRISMA guidelines. MEDLINE, CINAHL, EMBASE, and PsycInfo were searched from inception to December 31, 2018. Data related to the domains of each screening tool were extracted and mapped onto a framework based on the biopsychosocial model of Lehmans et al. (2009) and Wade & Halligans (2017). Results Sixty-seven frailty screening tools were captured in 79 articles. All screening tools assessed biological factors, 73% assessed psychological factors, 52% assessed social factors, and 78% assessed contextual factors. Under half (43%) of the tools evaluated all four domains, 33% evaluated three of four domains, 12% reported two of four domains, and 13% reported one domain (biological). Conclusion This review found considerable variation in the assessment domains covered by frailty screening tools. Frailty is a broad construct, and frailty screening tools need to cover a wide variety of domains to enhance screening and outcomes assessment.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| 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 it