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Record W4405189315 · doi:10.1002/ana.27151

Critical Care Decisions After Large Core Cerebral Infarctions: A Secondary Analysis From the <scp>SELECT2</scp> Trial

2024· article· en· W4405189315 on OpenAlexaff
Scott E. Kasner, Michael T. Mullen, Michael DeGeorgia, Spiros Blackburn, Donna K. George, Monisha A. Kumar, Steven R. Messé, Michael Abraham, Michael Chen, Clark Sitton, Jan‐Karl Burkhardt, Muhammad Shazam Hussain, Leonid P. Churilov, Sophia Sundararajan, Yin Hu, Nabeel Herial, Daniel J. Gibson, Juan F. Arenillas, Jenny P. Tsai, Ronald F. Budzik, William J. Hicks, Osman Kozak, Bernard Yan, Dennis Cordato, Nathan Manning, Mark Parsons, Ricardó A. Hanel, Amin Aghaebrahim, Teddy Y. Wu, Natàlia Pérez de la Ossa, Joanna D. Schaafsma, Jordi Blasco, Navdeep Sangha, Steven Warach, Chirag D. Gandhi, Timothy Kleinig, Daniel H. Sahlein, Edgar A. Samaniego, Laith Maali, Mohammad A Abdulrazzak, Krishna Amuluru, Deep Pujara, Faris Shaker, Hannah Johns, Rami Moussa, Faisal Al‐Shaibi, Stavropoula Tjoumakaris, Amanda Opaskar, Wei Xiong, Abhishek Ray, Sepideh Amin‐Hanjani, Thanh N. Nguyen, Johanna T. Fifi, Stephen M. Davis, Lawrence R. Wechsler, Anthony J. Furlan, Cathy Sila, Nicholas C. Bambakidis, Michael D. Hill, Vítor Mendes Pereira, Maarten G. Lansberg, James C. Grotta, Marc Ribó, Gregory W. Albers, Bruce Campbell, Ameer E Hassan, Amrou Sarraj

Bibliographic record

VenueAnnals of Neurology · 2024
Typearticle
Languageen
FieldMedicine
TopicAcute Ischemic Stroke Management
Canadian institutionsSt. Michael's HospitalUniversity of CalgaryToronto Western Hospital
Fundersnot available
KeywordsMedicineOdds ratioRandomized controlled trialInternal medicineRandomizationStroke (engine)PopulationCardiology

Abstract

fetched live from OpenAlex

OBJECTIVE: Among patients with large vessel occlusion (LVO) and large ischemic cores, critical decisions often need to be made about decompressive hemicraniectomy (DHC) or early withdrawal of life-sustaining therapy (WLST). In this study, we aimed to evaluate utilization of DHC and early WLST and factors associated with them in patients with large strokes from the SELECT2 trial. METHODS: We analyzed the entire SELECT2 trial population, which randomized 352 patients with stroke due to LVO and large ischemic cores to endovascular thrombectomy (EVT) or medical management. We used the as-treated principle to compare the use of DHC and early WLST within 7 days after randomization. We further assessed functional outcomes (modified Rankin Score) after these decisions. RESULTS: Of 352 patients enrolled in this study, 55 received DHC and 81 transitioned to early WLST. Patients treated with EVT were as likely to undergo DHC (16% vs 15%, adjusted relative risk [aRR] = 1.19, 95% CI:0.75-1.88, p = 0.46) or WLST (22% vs 24%, aRR = 0.94, 95% CI: 0.66-1.34, p = 0.72) as those given medical management. DHC was used more frequently in younger patients and WLST more in older patients. EVT efficacy was maintained after adjusting for DHC (adjusted generalized odds ratio [aGenOR] = 1.68, 95% CI: 1.24-2.11, p < 0.001), with no interaction between DHC and treatment (p-interaction = 0.93). At 1 year, 21% of DHC-treated patients were ambulatory; the outcomes were universally poor after early WLST. INTERPRETATION: In the SELECT2 trial of patients with large ischemic core, DHC was performed in ~1 of 6 patients and early WLST in ~1 of 5 patients, without differences based on treatment with EVT or medical management, nor successful reperfusion. DHC or WLST did not detract from thrombectomy treatment benefit. Additionally, ~20% of patients achieved independent ambulation despite receiving DHC by the 1-year follow-up. The similar distribution of these critical care decisions provides reassurance that the overall trial outcomes were not biased by open-label treatment allocation. ANN NEUROL 2025;97:698-708.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.009
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.004
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.000

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.

Opus teacher head0.052
GPT teacher head0.356
Teacher spread0.304 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations5
Published2024
Admission routes1
Has abstractyes

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