Étude du rôle pronostique des déconnexions cérébrales chez les patients présentant une occlusion de l'artère basilaire
Bibliographic record
Abstract
Background: prognostication in acute basilar artery occlusion (BAO) is difficult, particularly in patients with mild deficits, as current predictors lack accuracy. We evaluated whether disconnected white matter fiber volume, the disconnectome estimated indirectly from acute diffusion-weighted imaging (DWI) without patient-specific tractography, improves outcome prediction and identifies responders to recanalization compared with conventional metrics. Methods: consecutive adults (2017–2024) from the multicenter ETIS registry (NCT03776877) with BAO and baseline Magnetic resonance imaging (MRI) were analyzed. Infarcts were delineated on DWI, normalized to l'espace Montreal Neurological Institute's 152 (MNI152) space, and projected onto ultra–high-resolution normative tractograms from two healthy participants to estimate disconnected fiber volume. The primary outcome was 90-day modified Rankin Scale (mRS) 0–3 vs 4–6. Predictive performances were compared with baseline national institutes of health stroke scale (NIHSS), infarct volume, and posterior circulation Alberta Stroke program early CT score (pc-ASPECTS) using logistic regressions and AUCs. Ordinal regression assessed effects across the full mRS spectrum stratified by recanalization status. Analyses were repeated in patients with NIHSS ≤10. Results: Among 201 patients (median age 70; NIHSS 10), 51.7% had good outcomes; mortality was 35.3%. Median infarct volume was 4.75 mL, whereas median disconnected fiber volume was 25.15 mL. Disconnected fiber volume achieved an AUC of 0.84 (95% CI, 0.78–0.89), outperforming baseline NIHSS (0.67; p<0.0001), infarct volume (0.75; p=0.00059), and pc-ASPECTS (0.76; p=0.0127). Using a high-certainty threshold (>0.7 or <0.3 predicted probability), it correctly reclassified 80/96 uncertain cases vs infarct volume and 47/58 vs pc-ASPECTS. Low disconnected fiber volume predicted better outcomes across the mRS (OR=0.12; p<0.001) and greater benefit from successful recanalization (OR=0.33; p=0.005). In patients with NIHSS ≤10 (n=102), it remained the strongest predictor (AUC=0.83). Conclusions: disconnected fiber volume derived from acute DWI outperforms conventional predictors of BAO outcomes, including in mildly affected patients, and may inform treatment decisions. Prospective validation and clinical implementation are warranted.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".