P.085 The effect of remote ischemic conditioning on cognitive and radiological outcomes in cerebrovascular diseases: a systematic review
Bibliographic record
Abstract
Background: The effect of remote ischemic conditioning on cognitive and radiological outcomes in patients with cerebrovascular disease is uncertain. We performed a systematic review to evaluate the effects of remote ischemic conditioning on cognitive and radiological outcomes in patients with cerebrovascular diseases. Methods: A systematic search using relevant keywords and database specific terms was conducted in MEDLINE, Embase, and Web of Science from inception to October 27 2022. Results: A total of 4269 articles were screened, of which 20 were included. Ten papers assessing cognitive outcomes were included, with 5/10 reporting improvements in cognitive scores relative to a control group. Sixteen papers reporting on radiologic outcomes were included, three reporting on changes in infarct volume, and four on lesion volume. Improvement in infarct and lesion volume was reported in 1/3 studies and 4/4 studies respectively, however there was considerable variation in the time between assessments (range 1-365 days). Nine papers assessing blood flow changes were found, of which 3/8 using transcranial doppler reported improved blood flow velocity post intervention. Conclusions: The articles identified suggest that remote ischemic conditioning may provide improvement for both cognitive and radiological outcomes in patients with cerebrovascular diseases, however future well-designed studies are needed to determine degree of benefit.
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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 | 0.005 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.007 | 0.009 |
| 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) | 0.010 | 0.001 |
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".