Abstract 155: Critical Care Decisions After Large Core Cerebral Infarctions: <i>A Secondary Analysis From the SELECT2 Trial</i>
Bibliographic record
Abstract
Background/hypothesis: The SELECT2 trial randomly assigned patients with LVO and large ischemic cores to either endovascular thrombectomy (EVT) or medical management (MM). This population is at high risk for cerebral edema and other complications, often leading to critical decisions about decompressive hemicraniectomy (DHC) or early withdrawal of care (WOC). We hypothesized that patients initially treated with EVT were more likely to get life-sustaining care regardless of recanalization success, presumably because initial treatment with EVT led toward an expectation of aggressive care, while those treated with MM expected futility. Methods: We analyzed the full SELECT2 study population using the as-treated principle, comparing the use of DHC and early (within 7 days from randomization) transition to comfort measures/WOC. We also compared these decisions based on recanalization success in those receiving EVT. We further tested baseline characteristics for association with these outcomes. Results: Patients treated with EVT were as likely to undergo DHC (aRR:1.19 [0.75-1.88], p=0.46) or WOC (aRR:0.94 [0.66-1.34], p=0.72) as those given MM (Table). Time to DHC was also similar (EVT 47[19-74] vs. MM 36[27-61] hours, p=0.95). Patients with successful (mTICI≥2b) recanalization were numerically less likely to undergo DHC than those with unsuccessful (TICI 0-2a) recanalization (aRR:0.66 [0.33-1.3], p=0.23), while WOC was similar (Table). Larger estimated core volumes were associated with both DHC and WOC, with DHC used more in younger and WOC more in older patients. Conclusions: In the SELECT2 trial of patients with large infarct cores, DHC was performed in ~1 in 6 and WOC in ~1 in 5, without an observed difference based on treatment with EVT or MM. The similar distribution of decisions to proceed with DHC or to change goals of care to acute palliative measures provides reassurance that the overall trial outcomes were not biased by open-label treatment allocation.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.005 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 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".