TICI and Age: What's the Score?
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Previous studies have suggested that advanced age predicts worse outcome following mechanical thrombectomy. We assessed outcomes from 2 recent large prospective studies to determine the association among TICI, age, and outcome. MATERIALS AND METHODS: Data from the Solitaire FR Thrombectomy for Acute Revascularization (STAR) trial, an international multicenter prospective single-arm thrombectomy study and the Solitaire arm of the Solitaire FR With the Intention For Thrombectomy (SWIFT) trial were pooled. TICI was determined by core laboratory review. Good outcome was defined as an mRS score of 0-2 at 90 days. We analyzed the association among clinical outcome, successful-versus-unsuccessful reperfusion (TICI 2b-3 versus TICI 0-2a), and age (dichotomized across the median). RESULTS: Two hundred sixty-nine of 291 patients treated with Solitaire in the STAR and SWIFT data bases for whom TICI and 90-day outcome data were available were included. The median age was 70 years (interquartile range, 60-76 years) with an age range of 25-88 years. The mean age of patients 70 years of age or younger was 59 years, and it was 77 years for patients older than 70 years. There was no significant difference between baseline NIHSS scores or procedure time metrics. Hemorrhage and device-related complications were more common in the younger age group but did not reach statistical significance. In absolute terms, the rate of good outcome was higher in the younger population (64% versus 44%, P < .001). However, the magnitude of benefit from successful reperfusion was higher in the 70 years of age and older group (OR, 4.82; 95% CI, 1.32-17.63 versus OR 7.32; 95% CI, 1.73-30.99). CONCLUSIONS: Successful reperfusion is the strongest predictor of good outcome following mechanical thrombectomy, and the magnitude of benefit is highest in the patient population older than 70 years of age.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".