Not All “Successful” Angiographic Reperfusion Patients Are an Equal Validation of a Modified TICI Scoring System
Bibliographic record
Abstract
Rapid reperfusion of the entire territory distal to vascular occlusions is the aim of stroke interventions. Recent studies defined successful reperfusion as establishing some perfusion with distal branch filling of <50% of territory visualized (Thrombolysis In Cerebral Infarction "TICI" 2a) or more. We investigate the importance of the quality of final reperfusion and whether a revision of the successful reperfusion definition is warranted. We retrospectively evaluated a prospective database of anterior circulation strokes treated using stentrievers to assess the quality of final reperfusion using two scores: the traditional TICI score and a modified TICI score. The modified TICI score includes an additional category (TICI 2c): near complete perfusion except for slow flow or distal emboli in a few distal cortical vessels. We compared different cut-off definitions of reperfusion (TICI 2a - 3 vs. TICI-2b-3 vs. TICI 2c-3) using the area under the curve to identify their correlation with a favorable 90-day outcome (mRS≤2). In our cohort of 110 patients, 90% achieved TICI 2a-3 reperfusion with 80% achieving TICI 2b-3 and 55.5% achieving TICI 2c-3. The proportion of patients with a favorable 90-day outcome was higher in the TICI 2c (62.5%) compared to TICI 2b (44.4%) or TICI 2a (45.5%) but similar to the TICI 3 group (75.9%). A TICI 2c-3 reperfusion had a better predictive value than TICI 2b-3 for 90-day mRS 0-1. Defining successful reperfusion as TICI 2c/3 has merits. In this cohort, there was evidence toward faster recovery and better outcomes in patients with the TICI 2c vs. the traditional TICI 2b grade.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".