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Record W3158810028 · doi:10.1161/str.51.suppl_1.18

Abstract 18: Diffusion Weighted Imaging Lesions in Patients With Acute Intracerebral Hemorrhage: A Pooled Analysis of Individual Patient Data From MISTIE-III, ATACH-II, I-DEF, and ERICH

2020· article· en· W3158810028 on OpenAlexaff
Santosh B. Murthy, Sung‐Min Cho, Ajay Gupta, Ashkan Shoamanesh, Radhika Avadhani, Joshua Gruber, Tatiana Greige, Vasileios‐Arsenios Lioutas, Casey Norton, Pitchaiah Mandava, Guido J. Falcone, Kevin N. Sheth, Adnan I. Qureshi, Joshua N. Goldstein, Chelsea S. Kidwell, Magdy Selim, Daniel Woo, Hooman Kamel, Wendy Ziai, Daniel F. Hanley

Bibliographic record

VenueStroke · 2020
Typearticle
Languageen
FieldMedicine
TopicMRI in cancer diagnosis
Canadian institutionsPopulation Health Research Institute
Fundersnot available
KeywordsMedicineIntracerebral hemorrhageModified Rankin ScaleLeukoaraiosisMagnetic resonance imagingLogistic regressionInternal medicineEtiologyOdds ratioStroke (engine)HyperintensityRadiologyIschemic strokeSubarachnoid hemorrhageIschemia

Abstract

fetched live from OpenAlex

Introduction: The etiology and significance of diffusion weighted imaging (DWI) lesions in patients with acute intracerebral hemorrhage (ICH) remain unclear. We evaluated which factors were associated with DWI lesions, whether associated factors differed by ICH location, and whether DWI lesions were associated with functional outcomes. Methods: We pooled individual patient data from the MISTIE III trial, the ATACH-II trial, the i-DEF trial, and the ERICH study. We included only patients who underwent protocolized magnetic resonance imaging (MRI) of the brain. A poor functional outcome was defined as a modified Rankin Scale (mRS) score of 4-6 at 3-6 months. We used mixed effects logistic regression with the study database as a random effect. Results: Among 1,775 ICH patients, there were 621 (35.6%) lobar, 978 (55.9%) deep, and 148 (8.5%) infratentorial ICHs. Median time to MRI scan was 1.5 days (IQR, 1-4). DWIHLs occurred in 559 (31.5%) patients, with 190 (34.3%) in lobar ICH and 320 (57.8%) in deep ICHs. In mixed effects regression models, factors associated with DWIHLs included younger age factors associated with DWIHLs after acute ICH included younger age (OR, 0.98; 95% CI, 0.97-0.99), black race (OR, 1.59; 95% CI, 1.18-2.16), admission systolic blood pressure (SBP per 10 mm Hg, OR, 1.13; 95% CI, 1.05-1.22), cerebral microbleeds (OR, 1.71, 95% CI, 1.24-2.35), and leukoaraiosis (OR, 1.60; 95% CI, 1.14-2.25). Patients with DWIHLs had higher odds of mRS 4-6 (OR, 1.57; 95% CI, 1.24-1.99) compared to those without, after adjustment for demographics and ICH severity. In subgroup analyses, similar factors influenced DWIHLs in deep ICH. However, in lobar ICH, younger age, admission SBP, and leukoaraiosis were associated with DWIHLs. Presence of DWIHLs was independently associated with poor mRS in deep ICH but not in lobar ICH. There was no relationship between acute BP lowering and DWIHLs, regardless of location. Conclusions: In a large, heterogeneous cohort of ICH patients, our results are consistent with the hypothesis that DWIHLs represent the effects of chronic hypertensive vasculopathy and acute blood pressure elevation. Furthermore, DWIHLs portend poor prognosis after ICH, particularly in deep hemorrhages.

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)0.030
metaresearch head score (Gemma)0.031
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.030
Threshold uncertainty score0.159

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0300.031
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0060.013
Bibliometrics0.0030.003
Science and technology studies0.0000.000
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.020
GPT teacher head0.269
Teacher spread0.249 · 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 designMeta-analysis
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".

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Citations0
Published2020
Admission routes1
Has abstractyes

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