Grading of acute spontaneous cerebral hemorrhage combined with gut dysfunction assessment as a novel and improved prognostic score
Bibliographic record
Abstract
Objective This study aimed to identify independent predictive factors for 6-month outcomes in acute intracerebral hemorrhage (ICH) patients and to develop a novel prognostic model incorporating gut dysfunction duration. Methods This retrospective analysis included 245 ICH patients, comparing 157 with favorable outcomes and 88 with poor outcomes. Logistic regression was used to identify independent predictors. A new model (ID-ICH) was developed by incorporating gut dysfunction duration into the ICH Grading Scale (ICH-GS). The model’s performance was evaluated using the area under the receiver operating characteristic curve (AUC) and calibration analysis. Results The poor-outcome group was older, had higher ICH-GS and National Institutes of Health Stroke Scale (NIHSS) scores, and longer gut dysfunction duration. Prolonged gut dysfunction duration (OR = 1.239, P < 0.001) was identified as an independent predictor of poor outcomes. The ID-ICH model (AUC = 0.718) outperformed the conventional ICH-GS model (AUC = 0.628). Conclusions Prolonged gut dysfunction is an independent predictor of poor outcomes in ICH patients. The ID-ICH model improves prognostic accuracy and could assist in individualized management
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".