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Record W4281724357 · doi:10.1161/svin.121.000252

Predictors of Decompressive Hemicraniectomy in Successfully Recanalized Patients With Anterior Circulation Emergency Large‐Vessel Occlusion

2022· article· en· W4281724357 on OpenAlexaboutno aff
Daniel M. Heiferman, Georgios Tsivgoulis, Savdeep Singh, Diana Alsbrook, Ghaida Zaid, Leila Gachechiladze, Balaji Krishnaiah, Violiza Inoa‐Acosta, Nickalus R. Khan, Christopher Nickele, Daniel Hoit, Andrei V. Alexandrov, Lucas Elijovich, Adam S Arthur, Nitin Goyal

Bibliographic record

VenueStroke Vascular and Interventional Neurology · 2022
Typearticle
Languageen
FieldMedicine
TopicAcute Ischemic Stroke Management
Canadian institutionsnot available
FundersHealth Science Center, University of Tennessee
KeywordsInterquartile rangeMedicineThrombolysisCardiologyInternal medicineConfoundingStroke (engine)SurgeryMyocardial infarction

Abstract

fetched live from OpenAlex

Background Mechanical thrombectomy (MT) has been shown to improve functional outcome in patients with anterior circulation strokes and emergent large‐vessel occlusion (ELVO). Despite successful recanalization, some of these patients require decompressive hemicraniectomy (DHC). We aimed to study the predictors of DHC in successfully recanalized anterior circulation ELVO patients. Methods Consecutive patients with anterior circulation ELVO treated with MT during a 6‐year period were evaluated. Only successfully recanalized patients (modified Thrombolysis in Cerebral Infarction grades 2b, 2c, or 3) after MT were included in the analysis. Baseline demographic, clinical, and procedural variables were compared between patients requiring DHC after successful recanalization versus those who did not. Results Of 453 successfully recanalized patients with ELVO, 47 who underwent DHC had higher admission blood glucose levels (170±88 versus 142±66 mg/dL; P =0.008), lower median Alberta Stroke Program Early CT Scores (9 [interquartile range, 8–10] versus 10 [interquartile range, 9–10]; P =0.002), higher prevalence of poor collaterals on pretreatment computed tomography angiogram (75% versus 26%; P <0.001), and required more passes during MT (median, 3 [interquartile range, 3–4] versus 2 [interquartile range, 1–2]; P =0.001) compared with those who did not undergo DHC. In a multivariable model after adjusting for multiple confounders, higher admission blood glucose levels ( P =0.031), poor collaterals on computed tomography angiography ( P <0.001), and higher number of passes during MT ( P <0.001) emerged as independent predictors of DHC in successfully recanalized patients with ELVO. Conclusions Higher admission blood glucose levels, poor collateral pattern on computed tomography angiography, and higher number of passes during MT were independently associated with DHC in patients with anterior circulation ELVO achieving successful recanalization following MT.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.005
GPT teacher head0.232
Teacher spread0.227 · 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 teacher head, not a consensus.

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

Citations3
Published2022
Admission routes1
Has abstractyes

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