THE PREVALENCE OF FRAILTY IN STROKE, A SYSTEMATIC REVIEW
Bibliographic record
Abstract
Frailty, the clinical syndrome, can be defined by using either the phenotypical or the deficit model. Linda Fried proposed the former in 2001. John Rockwood in 1999 proposed the deficit model. There are several instruments that can be used to define frailty. We sought to determine the prevalence of frailty in acute stroke patients.We searched MEDLINE, EMBASE and CINAHL up to November 2018, using a pre-defined search criteria, in accordance with PRISMA guidance. Studies that used a defined measure of frailty were included. Studies that included transient ischaemic attack patients were excluded.A total of 1981 studies were screened, 9 studies met the inclusion criteria. Six studies were conference abstracts found in the grey literature. 1331 patients were included in total, of which 452 ( 34%) were said to be frail. Five different methods, for assessing frailty were used; the Clinical Frailty Scale, Rockwood Frailty index, Fried Frailty Criteria, Canadian Study on Health and Ageing Scale and the Reported Edmonton Frailty Scale were used.The review was limited by the small number of studies, this may be because frailty is a relatively new concept. Studies which included pre-morbid function were not included, if there was no measure of frailty. Our study suggests the need for further larger well designed studies that measure the prevalence of frailty in stroke patients and how frailty affects patient outcomes. Further work is also needed into what assessment method for frailty should be used
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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 | 0.009 | 0.046 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.006 |
| Bibliometrics | 0.021 | 0.020 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".