Infarct in new territory after endovascular stroke treatment: A diffusion-weighted imaging study
Bibliographic record
Abstract
Data on infarcts in new territory (INT) in patients undergoing endovascular stroke treatment for acute large-vessel occlusions are sparse. Aim of this study was to assess the prevalence, risk factors, and clinical relevance of INT. For this purpose, all patients in a single-center prospective registry who underwent endovascular stroke treatment and received pre- and post-interventional diffusion-weighted imaging were included (N = 259). Using an established scoring system, INT were classified according to size (I-III, ≤2 mm, >2 mm ≤20 mm, >20 mm) and likelihood of being related to the intervention (A, high likelihood; B, low likelihood). Additionally, a new type of infarct, that occurred in a territory distal to the occlusion, but was initially not hypoperfused, was defined as an infarct in initially not hypoperfused territory (IINHT). A total of 180 INT and 38 IINHT were observed in 32.8% (N = 85/259) of patients. In most patients, INT were angiographically occult (90.2%), and 13 patients had INT/IINHT larger than 2 cm (type III). Absence of protection during stent-retrieval and a cardio-embolic stroke origin were associated with higher incidence of INT/IINHT, whereas pretreatment with IV tPA showed no association, even when different bolus timing was considered. INT/IINHT were associated with lower rates of functional independence with increasing size type after adjusting for confounders (adjusted Odds Ratio per size group increase 0.63, 95% confidence interval 0.46-0.86). In conclusion, INT and IINHT are not rare, are associated with poor outcome with increasing size, and they may serve as a surrogate endpoint for safety evaluation of new devices and endovascular techniques. Further research on associated factors is warranted.
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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 | 0.000 | 0.002 |
| 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 | 0.001 | 0.001 |
| 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".