Remote Ischemic Conditioning and Stroke Recovery
Bibliographic record
Abstract
Remote ischemic conditioning (RIC) is a noninvasive procedure whereby several periods of ischemia are induced in a limb. Although there is growing interest in using RIC to improve stroke recovery, preclinical RIC research has focused exclusively on neuroprotection, using male animals and the intraluminal suture stroke model, and delivered RIC at times not relevant to either brain repair or behavioral recovery. In alignment with the Stroke Recovery and Rehabilitation Roundtable, we address these shortcomings. First, a standardized session (5-minute inflation/deflation, 4 repetitions) of RIC was delivered using a cuff on the contralesional hindlimb in both male and female Sprague-Dawley rats. Using the endothelin-1 stroke model, RIC was delivered once either prestroke (18 hours before, pre-RIC) or poststroke (4 hours after, post-RIC), and infarct volume was assessed at 24 hours poststroke using magnetic resonance imaging. RIC was delivered at these times to mimic the day before a surgery where clots are possible or as a treatment similar to tissue plasminogen activator, respectively. Pre-RIC reduced infarct volume by 41% compared with 29% with post-RIC. RIC was neuroprotective in both sexes, but males had a 46% reduction of infarct volume compared with 23% in females. After confirming the acute efficacy of RIC, we applied it chronically for 4 weeks, beginning 5 days poststroke. This delayed RIC failed to enhance poststroke behavioral recovery. Based on these findings, the most promising application of RIC is during the hyperacute and early acute phases of stroke, a time when other interventions such as exercise may be contraindicated.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".