The combination model of serum occludin and clinical risk factors improved the efficacy for predicting hemorrhagic transformation in stroke patients with recanalization
Bibliographic record
Abstract
Background and Purpose: Hemorrhagic transformation (HT) is one of the severe complications in acute ischemic stroke, especially for the patients who undergo recanalization treatment. It is crucial to screen patients who have high risk of HT before recanalization. However, current prediction models based on clinical factors are not ideal for clinical practice. Serum occludin, a biomarker for cerebral ischemia-induced blood-brain barrier disruption, has potential for predicting HT. This study was to investigate whether the combination of serum occludin and clinical risk factors improved the efficacy of predicting HT. Methods This was a single-center prospective observational study. Baseline clinical data and blood samples of recanalization patients were collected upon admission to our hospital. The level of serum occludin was measured using enzyme-linked immunosorbent assay. The diagnosis of HT was confirmed by CT scans within 36 h post recanalization. Results A total of 324 patients with recanalization were enrolled and 68 patients presented HT occurrence. HT patients had the higher level of baseline occludin than patients without HT ( p < 0.001). Multivariate regression analysis showed that serum occludin level, Alberta Stroke Program Early CT Scores and endovascular therapy were independent risk factors ( p < 0.05) for HT after adjusting potential confounders. The combination of serum occludin and clinical risk factors significantly improved the accuracy of predicting HT [area under the curve (AUC, 0.821 vs 0.701, p < 0.001), and net reclassification improvement (31.1 %), integrated discrimination improvement (21.5 %), p < 0.001] compared to a model employing only clinical risk factors. The modified AUC (0.806) of combined model based on 10-fold-cross-validation was still higher than clinical risk model (0.701). Conclusion The combination of serum occludin and clinical risk factors significantly improved the prediction efficacy for HT, providing a novel potential prediction model to screen for patients with high risk of HT before recanalization in acute ischemic stroke.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".