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Record W2759809437 · doi:10.1161/str.43.suppl_1.a100

Abstract 100: Small Intracerebral Hematomas Have A Low Spot Sign Prevalence And Are Unlikely To Expand

2012· article· en· W2759809437 on OpenAlexaff
Dar Dowlatshahi, Richard I. Aviv, David Luna Rodriguez, Carlos A. Molina, Imanuel Dzialowski, Anna Członkowska, Jean-Martin Boulanger, Cheemun Lum, Gord Gubitz, Vasantha Padma, Jayanta Roy, Carlos S. Kase, Teri Stewart, Rohit Bhatia, Matthew Boyko, Jayme C. Kosior, David J. Gladstone, Michael D. Hill, Andrew M. Demchuk

Bibliographic record

VenueStroke · 2012
Typearticle
Languageen
FieldMedicine
TopicIntracerebral and Subarachnoid Hemorrhage Research
Canadian institutionsUniversity of CalgaryUniversité de SherbrookeUniversity of TorontoDalhousie UniversityUniversity of Ottawa
Fundersnot available
KeywordsMedicineIntracerebral hemorrhageHematomaObservational studyOdds ratioProspective cohort studyCohort studyCohortStroke (engine)Internal medicineSurgeryGlasgow Coma Scale

Abstract

fetched live from OpenAlex

Background: Early intracerebral hemorrhage (ICH) expansion is a major determinant of poor clinical outcome. We previously reported baseline hematoma volume was a predictor of hematoma expansion (HE), and that hematomas <3mL may represent a subgroup with good prognosis. Our objective was to validate our previous findings in a multi-centre prospective observational cohort, and to assess the relationship between baseline hematoma size and the CTA spot-sign. We hypothesized that small hematomas are less likely to expand, and have low spot-sign prevalence. Methods: The PREDICT study is a prospective, observational cohort study of consecutive patients with acute ICH. Inclusion criteria are age>18, symptom onset <6 hours, and baseline CT and CTA; exclusions are baseline ICH >100ml, planned ICH surgery within 24 hours, known secondary cause of ICH, known renal impairment, GCS<6, or premorbid disability or terminal illness. Scans were reviewed for spot sign presence/absence by a neuroradiologist blinded to outcomes and follow-up imaging. Volumes were measured by planimetry by a neurologist blinded to CTA images and outcomes. The predictor of interest was baseline hematoma volume which was stratified as <3mL, 3-9mL, 10-19mL, 20-29mL and >30mL based on our prior study. Primary outcome was significant HE defined as ≥6mL. We used multivariable models to calculate adjusted odds ratios (aOR) for HE. Findings: Two-hundred and sixty-eight patients were enrolled from 11 centers in 6 countries: HE analysis was limited to 228 patients with follow-up CT before rFVIIa or surgical intervention. Median baseline hematoma volume was 12.4ml, spot-sign was present in 26.8% of patients, and 25% of patients had HE of ≥6ml. HE and spot sign prevalence increased with increasing baseline hematoma volume (see table ) . Only one patient with volume <3ml had HE; the patient was on warfarin (INR 2.2) but spot negative. Two patients with volumes <3ml were spot positive, but neither had HE. When compared to hematomas >30ml, the aOR for HE was 0.09 for <3ml hematomas, 0.14 for 3-9ml, 0.49 for 10-20ml, and 1.83 for 20-30ml (p<0.001). Associations between baseline hematoma volume and clinical outcomes will be presented. Discussion: Our results validate baseline hematoma volume as a predictor of HE. Furthermore, spot sign prevalence is associated with baseline hematoma volume. These results can inform ICH trial design and clinical prognostication at the bedside: small hematomas have a low spot sign prevalence and are unlikely to expand ≥6 ml, even when spot positive. Conversely, half of hematomas >30ml are spot positive and will expand.

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)0.002
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.009
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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.

Opus teacher head0.029
GPT teacher head0.293
Teacher spread0.264 · 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".

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Citations0
Published2012
Admission routes1
Has abstractyes

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