Frailty and stroke: Global implications for assessment, research, and clinical care—A WSO scientific statement
Bibliographic record
Abstract
Frailty is common in stroke and has important disease- and treatment-modifying effects. The need to develop clinical practice and research for the impact of frailty on stroke is likely to increase in the coming decades as the global population ages, resulting in a higher burden of frailty that is likely to be borne disproportionately by lower- and middle-income countries.The global nature of frailty in stroke necessitates global action. This World Stroke Organization Scientific Statement synthesizes the current evidence relating to the prevalence and effects of frailty across the stroke pathway. Furthermore, it includes expert consensus on priority areas from a global panel: standardization of frailty assessments for research, explicit measurements of frailty (in addition to disability) in large clinical trials, dedicated studies investigating the treatment-modifying effects of frailty in acute stroke and secondary prevention, research investigating the impact of frailty on the different aspects of recovery and rehabilitation after stroke, and understanding the mechanisms underpinning the relationship between frailty and stroke for potential therapeutic exploitation.This scientific statement has been reviewed and approved by the World Stroke Organization Executive.
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.
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 | 0.030 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.010 | 0.014 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".