NIHSS Consciousness Score Combined with ASPECTS is a Favorable Predictor of Functional Outcome post Endovascular Recanalization in Stroke Patients
Bibliographic record
Abstract
Although revascularization rates after endovascular thrombectomy for large vessel acute ischemic stroke (AIS) are high (71%), only 46% of patients achieve functional independence at 90 days. The present study was designed to explore a new method for predicting the functional prognosis of AIS patients after endovascular recanalization. A total of 200 anterior circulation stroke patients who received endovascular therapy were enrolled. Logistic regression analysis of clinical characteristics on functional independence were performed. The predictive power of sub-items in National Institute of Health stroke scale (NIHSS) and the combination of NIHSS consciousness and Alberta Stroke Program Early CT Score (ASPECTS) on functional independence were assessed by Receiver Operating Characteristic (ROC) curves and the latter was compared with 3 previously published prediction models by AUC (the area under ROC curve). The AUC for the NIHSS consciousness score to predict functional independence was higher than whole NIHSS and other sub-items (0.716 v 0.705, 0.586, 0.573, 0.552 and 0.559). Low NIHSS consciousness score, high ASPECTS score, short time from onset to recanalization, and high rate of successful recanalization were demonstrated to be significantly associated with the functional independence (OR 0.697, 2.226, 0.994 and 28.643). The prediction power of the combination was significantly better than NIHSS and ASPECTS alone (AUC 0.793 v 0.705 and 0.752). Compared with 3 other prediction models, the combination was found to be the strongest predictor for functional independence (AUC 0.793 v 0.791, 0.671 and 0.564). NIHSS which has been shown to be a strong predictor of functional outcomes after endovascular recanalization is largely dependent on the consciousness component. NIHSS consciousness score combined with ASPECTS appears to be a favorable predictor of functional independence. These findings may have broad reaching effects for isolated centers around the world without advanced imaging for triage and prognostication.
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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 | 0.003 |
| 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 | 0.000 |
| 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".