P.030 Remote ischemic conditioning in acute ischemic stroke and small vessel disease – a feasibility study
Bibliographic record
Abstract
Background: We tested the hypothesis that delivering remote ischemic conditioning (RIC) with an adjunct tissue reflectance sensor (TRS) device may be feasible in patients with acute ischemic stroke (AIS) and cerebral small vessel disease (cSVD). Methods: AIS patients with neurological deficits within 7 days of symptom onset were screened for moderate to severe cSVD. Eligible patients were randomized 2:1 to receive intervention RIC or sham RIC (7 days). The primary outcome measure was intervention feasibility. It was assessed as an intervention-related comfort by a 5-point Likert scale during each session (1-very uncomfortable, 5-very comfortable). The secondary outcome measure was assessment of TRS derived dermal blood concentration and blood oxygenation changes during RIC. Results: Forty-seven (32 intervention, 15 sham) patients were enrolled at a median (IQR) 39.7 (25-64) hours after symptom onset, with mean±SD age of 75±12 years, 22 (46.8%) were females and median baseline NIHSS of 5(3-7). The Likert scale was 3.5 (3-4) in the intervention group and 4 (4-5) in the sham group. The TRS derived blood concentration and blood oxygenation changes were proportionate in the intervention arm and absent in the sham arm. Conclusions: RIC treatment with TRS is feasible in patients with AIS+cSVD. The efficacy of RIC needs further assessment.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 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".