Development and validation of a prognostic computed tomography scoring model for functional outcomes in patients with large hemispheric infarction following decompressive craniectomy
Bibliographic record
Abstract
Background There is no established prognostic scoring system developed for patients with large hemispheric infarction (LHI) following decompressive craniectomy (DC) based on imaging characteristics. The present study aimed to develop and validate a new computed tomography scoring model to assess the 6-month risk of poor functional outcomes (modified-Rankin scale [mRS] score of 4–6) in patients with LHI receiving DC. Methods This retrospective cohort study included patients at two tertiary stroke centers. A prediction model was developed based on a multivariable logistic regression. The final risk factors included the ASPECTS (Alberta Stroke Program Early Computed Tomography Score), longitudinal fissure cistern, Sylvian fissure cistern, and additional vascular territory involvement. 1,000 bootstrap resamples and temporal validation were implemented as validations for the scoring system. Results Of the 100 individuals included in the development cohort, 71 had poor functional outcomes. The scoring model presented excellent discrimination and calibration with C-index = 0.87 for the development cohort, and C-index = 0.83 for the temporal validation cohort with non-significant Hosmer-Lemeshow goodness-of-fit test. The scoring model also showed an improved AUC compared to the ASPECTS. For each point in the score model, the adjusted risk of poor functional outcomes increase by 47.8% (OR = 1.48, p < 0.001). The scores were inversely correlated with MAP (mean arterial pressure, paired t-test, p = 0.0015) and CPP (cerebral perfusion pressure, rho = −0.17, p = 0.04). Conclusion In patients with LHI following DC, the score system is an excellent predictor of poor functional outcomes and is associated with CPP and MAP, which might be worth considering in clinical settings after further external validation.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".