Predicting symptomatic intracranial hemorrhage in anterior circulation stroke patients with contrast enhancement after thrombectomy: the CAGA score
Bibliographic record
Abstract
BACKGROUND: The aim of the study was to establish a reliable scoring tool to identify the probability of symptomatic intracranial hemorrhage (sICH) in anterior circulation stroke patients with contrast enhancement (CE) on brain non-contrast CT (NCCT) after endovascular thrombectomy (EVT). METHODS: We retrospectively reviewed consecutive patients with acute ischemic stroke (AIS) who had CE on NCCT immediately after EVT for anterior circulation large vessel occlusion (LVO). We used the Alberta stroke program early CT score (ASPECTS) scoring system to estimate the extent and location of CE. Multivariable logistic regression was performed to derive an sICH predictive score. The discrimination and calibration of this score were assessed using the area under the receiver operator characteristic curve, calibration curve, and decision curve analysis. RESULTS: In this study, 194 of 322 (60.25%) anterior circulation AIS-LVO patients had CE on NCCT. After excluding 85 patients, 109 patients were enrolled in the final analysis. In multivariate regression analysis, age ≥70 years (adjusted OR (aOR) 9.23, 95% CI 2.43 to 34.97, P＜0.05), atrial fibrillation (AF) (aOR 4.17, 95% CI 1.33 to 13.12, P＜0.05), serum glucose ≥11.1 mmol/L (aOR 9.39, 95% CI 2.74 to 32.14, P＜0.05), CE-ASPECTS ＜5 (aOR 3.95, 95% CI 1.30 to 12.04 P＜0.05), and CE at the internal capsule (aOR 3.45, 95% CI 1.03 to 11.59, P＜0.05) and M1 region (aOR 3.65, 95% CI 1.13 to 11.80, P＜0.05) were associated with sICH. These variables were incorporated as the CE-age-glucose-AF (CAGA) score. The CAGA score demonstrated good discrimination and calibration in this cohort, as well as the fivefold cross validation. CONCLUSION: The CAGA score reliably predicted sICH in patients with CE on NCCT after EVT treatment.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 |
| 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".