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Record W4400351651 · doi:10.1136/jnis-2024-021725

Hemorrhagic transformation in acute ischemic stroke: hemorrhagic subtypes and symptomatic intracranial hemorrhage

2024· article· en· W4400351651 on OpenAlexfundno aff
Marie K. Luff, Nicole Khezri, Salvador Miralbés, Bharath Naravetla, Alejandro M Spiotta, Christian Loehr, Mario Martínez‐Galdámez, Ryan McTaggart, Luc Defreyne, Pedro Vega, Osama O. Zaidat, Lori Lyn Price, Rishi Gupta, Markus Möhlenbruch, David S. Liebeskind

Bibliographic record

VenueJournal of NeuroInterventional Surgery · 2024
Typearticle
Languageen
FieldMedicine
TopicIntracerebral and Subarachnoid Hemorrhage Research
Canadian institutionsnot available
FundersUniversity of California, IrvineWest Virginia UniversityUniversity of South CarolinaOttawa Hospital Research InstituteStrykerUniversity of OklahomaHealth Science Center, University of TennesseeTexas Tech UniversityThomas Jefferson UniversityKaiser Permanente
KeywordsMedicineSubarachnoid hemorrhageStroke (engine)HematomaIntraparenchymal hemorrhageIntracerebral hemorrhageCohortInternal medicineCardiologyRadiology

Abstract

fetched live from OpenAlex

BACKGROUND: Few clinical studies perform detailed analyses of subtypes of intracranial hemorrhage (ICH) after mechanical thrombectomy (MT) used to treat acute ischemic stroke. Symptomatic intracranial hemorrhage (sICH) is a formidable complication of MT and is widely used in clinical trials as a safety outcome. However, variable definitions of sICH are used across clinical studies. OBJECTIVE: To radiographically subcategorize post-MT ICH development within this large cohort and examine overlap with sICH. Second, to examine the agreement of this definition of sICH with local site-reported occurrences of sICH to see how sICH rates change with modifications of the definitions used. METHODS: A large cohort of patients treated with MT for acute ischemic stroke (n=1395) was analyzed to (1) radiographically characterize hemorrhagic subtypes of intracranial hemorrhage (ICH) occurring after MT; (2) examine associations of hemorrhagic subtypes with sICH; and (3) compare core laboratory-adjudicated occurrences of sICH with site-reported sICH. RESULTS: The overall rate of ICH was 552/1395 patients (39.6%), and the overall rate of sICH was 47/1395 (3.4%). The most common type of ICH was hemorrhagic infarction type 1 (HI1), which represented 45.3% of all ICH cases- followed by HI2 (31.5%) and subarachnoid hemorrhage (SAH, 29.2%). Parenchymal hematoma 2 (PH2) represented only 3.3% of all ICH cases. Of the PH2 hemorrhages, only 33.3% were determined to be symptomatic. Of sICH cases, the most common ICH subtypes were HI2 (48.9%) and SAH (38.3%). Comparison of sICH rates as determined by core laboratory adjudication versus local site-reported results showed that only 14 patients were identified as having sICH with both definitions, with 47 patients total with sICH according to one definition, but not the other. CONCLUSIONS: Results of this analysis demonstrate the radiographic subtypes of ICH and also highlight the limitations of variable criteria used to define sICH, suggesting that it might be appropriate to revisit how sICH is defined post-MT. TRIAL REGISTRATION NUMBER: Clinical trial NCT03845491.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.607
Threshold uncertainty score0.828

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.019
GPT teacher head0.287
Teacher spread0.268 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
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

Citations20
Published2024
Admission routes1
Has abstractyes

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