Prognostic factors for acute posterior circulation cerebral infarction patients after endovascular mechanical thrombectomy
Bibliographic record
Abstract
ABSTRACT: This article was to analyze the factors influencing the prognosis of posterior circulation cerebral infarction (PCCI) patients, retrospectively.One hundred forty five patients diagnosed with PCCI in Nanyang Central Hospital between June 25, 2016 and October 14, 2019 were included and underwent cerebral vascular mechanical thrombectomy. The clinical data of those patients were collected. The patients were followed up for 3 months to observe the prognostic efficacy and explore the influencing factors for poor prognosis. The potential prognostic factors for PCCI patients after emergency endovascular mechanical thrombectomy were analyzed by univariate and multivariable logistic regression. The thermodynamic diagram was drawn to explore the associations between the prognostic factors.The risk of poor prognosis in PCCI patients receiving emergency endovascular mechanical thrombectomy was reduced by 0.552 time with every 1-point increase of the Alberta Stroke Program Early CT in posterior circulation score (odds ratio [OR] = 0.448, 95% confidence interval [CI]: 0.276-0.727). The risk of poor prognosis was increased by 0.827 time for each additional grade in the digital subtraction angiography-American Society of Intervention and Therapeutic Neuroradiology grading (OR = 1.827, 95% CI: 1.221-2.733, P = .003) and increased by 0.288 time for every 1-point increase in National Institutes of Health Stroke scale at 24 hours (OR = 1.288, 95% CI: 1.161-1.429). All P < .05.Alberta Stroke Program Early CT in posterior circulation score, digital subtraction angiography-American Society of Intervention and Therapeutic Neuroradiology grading, National Institutes of Health Stroke scale score at 24 hours were factors affecting the prognosis of PCCI patients undergoing emergency endovascular mechanical thrombectomy, which might provide evidence for endovascular treatment of PCCI.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".