P.083 Early and 30-day clinical and neuropsychological effects of iatrogenic brain infarcts in the ENACT randomized-controlled trial
Bibliographic record
Abstract
Background: Small brain infarcts are often seen on diffusion-weighted MRI(DWI) following surgical/endovascular procedures. Little is known about their clinical effects. We examined the association of iatrogenic infarcts with outcomes in the ENACT(Evaluating Neuroprotection in Aneurysm Coiling Therapy) trial of nerinetide in endovascular aneurysm repair. Methods: In this post-hoc analysis, we used multi-variable models to evaluate the association of presence/number of DWI iatrogenic infarcts with NIHSS(National Institutes of Health Stroke Scale), mRS(modified Rankin Scale), and cognitive/neuropsychological scores(30-minute battery) at 1-4 and 30-days post-procedure. We also related infarct number to a Z-score-derived composite outcome score(quantile regression). Results: Among 185 patients(median age:56,IQR:50-64), 124(67.0%) had iatrogenic infarcts(median:4,IQR:2-10.5). Nerinetide resulted in fewer infarcts. Patients with infarcts had lower Mini-Mental State Exam(MMSE) scores at 2-4 days(median:28 vs 29, adjusted-coefficient[acoef] per additional infarct:-1.11,95%CI:-1.88 to -0.34,p=0.005). Infarct number was associated with worse day-1 NIHSS(aOR for NIHSS≥1:1.07,1.02-1.12,p=0.009), day 2-4 mRS(adjusted common odds-ratio[aOR]:1.05,1.01-1.09,p=0.005) and MMSE(acoef:-0.07,-0.13 to -0.003,p=0.040), 30-day mRS(aOR:1.04,1.01-1.07,p=0.016) and Hopkins Verbal Learning Test scores(acoef:-0.21,-0.39 to -0.03,p=0.020), as well as worse composite scores at 1-4 and 30-days(acoef:-0.09,-0.15 to -0.03,p=0.006). Conclusions: Iatrogenic infarcts were associated with subtle differences in post-procedural(1-4 days) and 30-day outcomes in this middle-aged cohort. Future studies should use batteries of similar/greater granularity to validate optimal measures for short- versus long-term manifestations.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 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".