ESOC 2023 – Late Breaking Abstracts
Bibliographic record
Abstract
Background and aims: Remote ischemic conditioning (RIC) with transient cycles of limb ischemia and reperfusion is cerebroprotective in preclinical models and some clinical stroke trials.We investigated whether combined prehospital and in-hospital RIC improves functional outcome in patients with acute stroke.Methods: We performed a multicenter, prospective, randomized, patient-assessor blinded, sham-controlled study.Eligible patients were adult, independent in activities of daily living, had prehospital stroke symptoms with a duration <4 hours.Patients were randomly assigned to RIC or sham.Treatment was started in the ambulance and continued in-hospital.The primary endpoint was improvement in functional outcome measured as shift across the modified Rankin Scale in the target population with a final diagnosis of ischemic or hemorrhagic stroke.Results: From March 16, 2018, to November 11, 2022, 1500 patients underwent prehospital randomization.Median age was 71, and 591 (41%) were female.Of these, 149 (10%) patients were diagnosed with transient ischemic attack and 382 (27%) with a stroke mimic.In the remaining 902 patients with a target diagnosis of stroke a total of 436 were treated with RIC and 466 with sham.Treatment with RIC was not associated with a shift towards better functional outcome at 90 days (Odds ratio, 1.05; 95% confidence interval,0.83-1.33,p=0.67).We found no significant effect on key secondary endpoints and no safety concerns.Conclusions: We did not show improvement on functional outcome of combined prehospital and in-hospital RIC among patients with acute stroke.Half the patients were included within the first hour after stroke onset.ClinicalTrials.gov:NCT03481777.
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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.949 | 0.897 |
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".