Efficacy of endovascular treatment for patients with acute large vessel occlusion stroke from the Western Sichuan Plateau and machine learning prediction models: a prospective study protocol
Bibliographic record
Abstract
Objectives: Stroke is the second leading cause of death and the third leading cause of disability among non-communicable diseases globally. The prevalence, incidence, and mortality rates of stroke are higher in high-altitude regions compared to lowland areas. However, compared to plain areas, the efficacy of endovascular therapy for large vessel occlusive acute ischemic stroke (LVO-AIS) in high-altitude regions remains unclear. Methods and Design: This study is a multicenter, prospective, endpoint-blinded cohort study. From January 2025 to December 2027, a total of 1,052 patients with acute large vessel occlusion ischemic stroke (LVO-AIS) from the Western Sichuan Plateau will be prospectively enrolled, including those who receive endovascular treatment and those who do not. Baseline characteristics and endovascular treatment details will be documented. Treatment decisions are guided by clinical practice guidelines, taking into account high-altitude real-world constraints such as patient or proxy refusal and delays in interhospital transfer. Medical records will be established for each patient, and a 180-day follow-up will be conducted. The primary outcome was the proportion of patients achieving functional independence [modified Rankin scale (mRS) range from 0 to 2] at 90 days. The secondary outcomes included the mRS score at 90 days, early neurological improvement rate [defined as a National Institutes of Health Stroke Scale (NIHSS) score of 0-2 or a reduction of ≥8 points from baseline within 24 h of enrollment], changes in NIHSS scores between day 7 ± 1 or discharge and baseline, quality of life as assessed by the five-level EuroQol five-dimensional questionnaire at 90 days, and cognitive function at 180 days will be assessed using mini-mental state examination and montreal cognitive assessment scores. Imaging outcomes will include the rate of successful reperfusion (defined as a modified Thrombolysis in Cerebral Infarction score ≥2b) and infarct volume measured within 5-7 days. Statistical analysis and fused optimized multimodal learning were blinded to the group assignments. Conclusion: This study aims to evaluate the efficacy of endovascular treatment compared with standard medical therapy in patients with LVO-AIS in the Western Sichuan Plateau and to develop an artificial intelligence-based prognostic model to refine treatment strategies for this and other high-altitude regions. Clinical trial registration: https://www.chictr.org.cn/showproj.html?proj=241870, ChiCTR2400092762.
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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 | 0.013 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".