Nomogram predicting early neurological improvement in ischaemic stroke patients treated with endovascular thrombectomy
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Early neurological improvement (ENI) after endovascular thrombectomy (EVT) has been associated with favorable outcomes. This study aimed to identify the optimal definition of ENI and develop a nomogram for predicting ENI after EVT in acute ischaemic stroke. METHODS: Patients with EVT were enrolled from a multicenter registry as the training cohort. The receiver operating characteristic curve was used to estimate the optimal threshold for ENI at 24 h of EVT. Logistic regression analysis was utilized to generate the best-fit nomogram for predicting ENI. The discrimination of the nomogram was assessed using the area under the receiver operating characteristic curve (AUC). An additional 447 patients from two stroke centers were prospectively recruited as the test cohort for validating the nomogram. RESULTS: A total of 612 patients with EVT were included in the training cohort. The optimal threshold for predicting 3-month favorable outcome (modified Rankin Scale 0-2) was an improvement of the National Institutes of Health Stroke Scale (NIHSS) score by ≥6 points (AUC 0.875; sensitivity 79.5%; specificity 90.7%). Age, blood glucose, recanalization, symptomatic intracranial hemorrhage (sICH) and baseline Alberta Stroke Program Early Computed Tomography Score (ASPECTS) were independently associated with ENI, and were incorporated in the nomogram. The AUC of the nomogram was 0.795 in the training cohort and 0.752 in the test cohort. CONCLUSIONS: A reduction of NIHSS score ≥6 appeared to be the optimal definition of ENI. The nomogram composed of age, blood glucose, recanalization, sICH and baseline ASPECTS may predict the probability of ENI in ischaemic stroke patients treated with EVT.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".