Predicting symptomatic intracranial hemorrhage after endovascular treatment of vertebrobasilar artery occlusion: PEACE score
Bibliographic record
Abstract
Background Current clinical decision tools for assessing the risk of symptomatic intracranial hemorrhage (sICH) in patients with vertebrobasilar artery occlusion (VBAO) who received endovascular treatment (EVT) have limited performance. This study develops and validates a clinical risk score to precisely estimate the risk of sICH in VBAO patients. Methods The derivation cohort recruited patients with VBAO who received EVT from the Posterior Circulation IschemIc Stroke Registry in China. Based on the posterior circulation-Alberta Stroke Program Early CT Score (pc-ASPECTS) evaluation method, the cohort was further divided into non-contrast CT (NCCT) and diffusion weighted imaging (DWI) cohorts to construct predictive models. sICH was diagnosed according to the Heidelberg Bleeding Classification within 48 hours of EVT. Clinical signature was constructed in the derivation cohort using machine learning and was validated in two additional cohorts from Asia and Europe. Results We enrolled 1843 patients who underwent EVT and had complete data. pc-ASPECTS of 1710 patients was evaluated on NCCT and 699 patients on DWI. In the NCCT cohort, 1364 individuals made up the training set, of whom 101 (7.4%) developed sICH. In the DWI cohort, the training set consisted of 560 individuals, with 44 (7.9%) experiencing sICH. Predictors of sICH were: glucose, pc-ASPECTS, time from estimated occlusion to groin puncture (EOT), poor collateral circulation, and modified Thrombolysis in Cerebral Infarction (mTICI) score. From these predictors, we derived the weighted poor collateral circulation-EOT-pc-ASPECTS-mTICI-glucose (PEACE) score. The PEACE score showed good discrimination in the training set (area under the curve (AUC) NCCT =0.85; AUC DWI =0.86), internal validation set (AUC NCCT =0.81; AUC DWI =0.82), and two additional external validation set (Asia: AUC NCCT =0.78, AUC DWI =0.80; Europe: AUC NCCT =0.74, AUC DWI =0.78). Conclusion The PEACE score reliably predicted the risk of sICH in VBAO patients who underwent EVT.
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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) | 0.001 | 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".