Thrombolysis in Cerebral Infarction 2b Reperfusions
Bibliographic record
Abstract
In patients undergoing mechanical thrombectomy, achieving complete (Thrombolysis in Cerebral Infarction 3) rather than incomplete successful reperfusion (Thrombolysis in Cerebral Infarction 2b) is associated with better functional outcome. Despite technical improvements, incomplete reperfusion remains the final angiographic result in 40% of patients according to recent trials. As most incomplete reperfusions are caused by distal vessel occlusions, they are potentially amenable to rescue strategies. While observational data suggest a net benefit of up to 20% in functional independence of incomplete versus complete reperfusions, the net benefit of secondary improvement from Thrombolysis in Cerebral Infarction 2b to 3 reperfusion might differ due to lengthier procedures and delayed reperfusion. Current strategies to tackle distal vessel occlusions consist of distal (microcatheter) aspiration, small adjustable stent retrievers, and administration of intra-arterial thrombolytics. While there are promising reports evaluating those techniques, all available studies show relevant limitations in terms of selection bias, single-center design, or nonconsecutive patient inclusion. Besides an assessment of risks associated with rescue maneuvers, we advocate that the decision-making process should also include a consideration of potential outcomes if complete reperfusion would successfully be achieved. These include (1) a futile angiographic improvement (hypoperfused territory is already infarcted), (2) an unnecessary angiographic improvement (the patient would not have developed infarction if no rescue maneuver was performed), and (3) a successful rescue maneuver with clinical benefit. Currently there is paucity of data on how these scenarios can be predicted and the decision whether to treat or to stop in a patient with incomplete reperfusion involves many unknowns. To advance the status quo, we outline current knowledge gaps and avenues of potential research regarding this clinically important question.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".