Predicting symptomatic intracranial haemorrhage after mechanical thrombectomy: the TAG score
Bibliographic record
Abstract
Background There is limited data on predictors of symptomatic intracranial haemorrhage (sICH) in patients who underwent mechanical thrombectomy. In this study, we aim to determine those predictors with external validation. Methods We evaluated mechanical thrombectomy in a derivation cohort of patients at a comprehensive stroke centre over a 30-month period. Clinical and radiographic data on these patients were obtained from the prospective quality improvement database. sICH was defined using the European Cooperative Acute Stroke Study III. We compared clinical and radiographic characteristics between patients with and without sICH using χ 2 and t tests to identify independent predictors of sICH with p<0.1. Significant variables were then combined in a multivariate logistic regression model to derive an sICH prediction score. This score was then validated using data from the Blood Pressure After Endovascular Treatment multicentre prospective registry. Results We identified 578 patients with acute ischaemic stroke who received thrombectomy, 19 had sICH (3.3%). Predictive factors of sICH were: thrombolysis in cerebral ischaemia (TICI) score, Alberta stroke program early CT score (ASPECTS), and glucose level, and from these predictors, we derived the weighted TICI-ASPECTS-glucose (TAG) score, which was associated with sICH in the derivation (OR per unit increase 1.98, 95% CI 1.48 to 2.66, p<0.001, area under curve ((AUC)=0.79) and validation (OR per unit increase 1.48, 95% CI 1.22 to 1.79, p<0.001, AUC=0.69) cohorts. Conclusion High TAG scores are associated with sICH in patients receiving mechanical thrombectomy. Larger studies are needed to validate this scoring system and test strategies to reduce sICH risk and make thrombectomy safer in patients with elevated TAG scores.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| 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 | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".