Prediction of stress-related gastrointestinal bleeding in patients with aneurysmal subarachnoid hemorrhage using caudate Hounsfield unit value in ASPECT score
Bibliographic record
Abstract
Background Stress-related gastrointestinal bleeding (SRGB) is one of the major complications after aneurysmal subarachnoid hemorrhage (aSAH), and it can present challenges in patient care and treatment. The aim of this study was to explore the clinical significance of the caudate Hounsfield unit (HU) value in the Alberta Stroke Program Early CT (ASPECT) score for predicting SRGB in patients with aSAH. Methods We retrospectively analyzed the data of 531 aSAH patients admitted to our institution between 2019 and 2022. Potential predictors of SRGB were identified using multivariate Cox regression analysis. We used a restricted cubic spline (RCS) to evaluate whether there is a nonlinear relationship between the right caudate HU value and SRGB. MaxStat analysis (titled as maximally selected rank statistics) was performed to identify the optimal cutoff point for the right caudate HU value. Another Kaplan–Meier method with the log-rank test was used to analyze the right caudate HU value in predicting the occurrence of SRGB. Results The incidence rate of SRGB was 17.9%. In the multivariate Cox regression analysis, the right caudate HU value was an independent predictor of SRGB [Hazard ratio (HR) = 0.913; 95% confidence interval (CI): 0.847–0.983, and p = 0.016]. The RCS indicated that the incidence of developing SRGB reduces with increasing right caudate HU values (nonlinear p = 0.78). The optimal cut-off value of the right caudate HU was 25.1. Conclusion Among aSAH patients, lower right caudate HU values indicated a higher risk of developing SRGB. Our findings provide further evidence for the relationship between the gastrointestinal system and the brain.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".