A Nomogram Prediction Model Based on Tissue Window for the Prognosisof Patients with Acute Ischemic Stroke Undergoing Thrombectomy
Bibliographic record
Abstract
Objective: Thrombectomy greatly improves the clinical prognosis of patients with acute ischemic stroke (AIS). The aim of this study is to develop a nomogram model that can predict the prognosis of patients with acute ischemic stroke undergoing thrombectomy. Methods: We retrospectively collected information of patients with acute ischemic stroke who were admitted to the stroke Green Channel of the First Affiliated Hospital of Soochow University from September 2018 to May 2022. The main outcome was defined as a three-month unfavorable outcome (modified Rankin Scale 3-6). Based on the results of multivariate regression analysis, a nomogram was established. We tested the accuracy and discrimination of our nomogram by calculating the consistency index (C-index) and plotting the calibration curve. Results: National Institutes of Health Stroke Scale (NIHSS) score (OR, 1.418; 95% CI, 1.177-1.707; P＜0.001), low density lipoprotein cholesterol (LDL-C) (OR, 2.705; 95% CI, 1.203-6.080; P = 0.016), Alberta Stroke Program Early Computed Tomography Score (ASPECTS) (OR, 0.633; 95% CI, 0.421-0.952; P = 0.028), infarct core volume (OR, 1.115; 95% CI, 1.043-1.192; P = 0.001) and ischemic penumbra volume (OR, 1.028; 95% CI, 1.006-1.050; P = 0.012) were independent risk factors for poor clinical prognosis of AIS patients treated with thrombectomy. The C-index of our nomogram was 0.967 and the calibration plot revealed a generally fit in predicting three-month unfavorable outcomes. Based on this nomogram, we stratified the risk of thrombectomy population. We found that low-risk population is less than or equal to 65 points, and patients of more than 65 points tend to have a poor clinical prognosis. Conclusion: The nomogram, composed of NIHSS, LDL-C, ASPECTS, infarct core volume and ischemic penumbra volume, may predict the clinical prognosis of cerebral infarction patients treated with thrombectomy.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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".