Factors associated with early neurological deterioration in minor distal medium vessel acute ischemic stroke: A multinational multicenter study
Bibliographic record
Abstract
Background: Patients with acute ischemic stroke secondary to distal and medium vessel occlusion (AIS-DMVO) and minor strokes present a challenge in determining the most appropriate emergent treatment. Factors leading to early neurological deterioration (END) in this patient population are understudied, but END is known to result in poor functional outcomes. Therefore, we aimed to investigate the factors contributing to END in minor AIS-DMVO cases. Methods: We included patients with AIS-DMVO and minor strokes from 37 sites across North America, Asia, and Europe. Minor stroke was defined as a baseline National Institutes of Health Stroke Scale (NIHSS) score of ⩽5. The primary outcome measure, END, was defined as a shift of ⩾4 points in the NIHSS score at day one after treatment compared to baseline. Univariable and multivariable logistic regression analyses were performed to identify factors associated with END. Results: Among 559 consecutive patients with DMVO and minor strokes, END was reported in 68 patients. In multivariable analysis, mechanical thrombectomy (MT) was independently associated with higher odds of END (adjusted odds ratio [aOR] 2.37, 95% CI 1.12–5.02, p = 0.02), while intravenous thrombolysis (IVT) was associated with lower odds of END (aOR 0.46, 95% CI 0.26–0.81, p = 0.008). However, the association between MT and END was no longer statistically significant in the IPTW-adjusted analysis (OR 1.65, 95% CI 0.69–3.98, p = 0.26). Hypertension and antiplatelet use at baseline were also independently associated with END. Among MT-treated patients, successful and excellent recanalization and first-pass effect were protective against END. Conclusion: MT was associated with END in patients with minor AIS-DMVO, although this association was not significant after IPTW adjustment. IVT was independently associated with reduced risk of END. These findings support careful patient selection and further study in randomized trials.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".