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Record W2951048444 · doi:10.1161/str.48.suppl_1.wmp49

Abstract WMP49: White Matter Hyperintensities in a High Risk Population Living in Marginal Housing (HOTEL study)

2017· article· en· W2951048444 on OpenAlexaff
Lily Zhou, William J. Panenka, Allen E. Thornton, Geoffrey S. Smith, Alasdair M. Barr, Alexander Rauscher, Donna Lang, Wayne Su, Kristina M. Gicas, Melissa L. Woodward, Tari Buchanan, Talia Vertinsky, Manraj K. S. Heran, Fidel Vila‐Rodriguez, G. William MacEwan, William G. Honer, Thalia S. Field

Bibliographic record

VenueStroke · 2017
Typearticle
Languageen
FieldMedicine
TopicIntracerebral and Subarachnoid Hemorrhage Research
Canadian institutionsMacEwan UniversitySimon Fraser UniversityUniversity of British Columbia Hospital
Fundersnot available
KeywordsMedicineHyperintensityLeukoaraiosisFluid-attenuated inversion recoveryInternal medicinePopulationOdds ratioCohortStroke (engine)Univariate analysisCardiologyMagnetic resonance imagingRadiologyMultivariate analysis

Abstract

fetched live from OpenAlex

Background: White Matter Hyperintensities (WMH) are features of cerebral small vessel disease (cSVD) along with lacunes, cerebral microbleeds and perivascular spaces. Vascular risk factors account for only a small proportion of the variability of the presence of WMH, and the role of additional risk factors including drug use/dependence or infections is not well defined. Objective: Examine prevalence and risk factors associated with WMH of presumed vascular origin within the HOTEL cohort, a population living in marginal housing with a high prevalence of prior homelessness, substance dependence, head trauma, mental illness and infectious diseases. Methods: Baseline imaging on 3T MRI included T1, T2-FLAIR and SWI sequences. WMH not consistent with vascular origins were excluded. Two raters assessed WMH using the Fazekas scale. Participants were divided into those with or without moderate-severe WMH (periventricular Fazekas score >2 or deep score >1). Potential cSVD risk factors which were significant on univariate analysis were entered into a multivariable stepwise binomial logistic regression to identify independent risk factors for moderate-severe WMH. Results: Intraclass coefficient for inter-rater reliability was 0.948 (95% CI, 0.924 to 0.965) for periventricular WMH and 0.848 (95% CI, 0.782 to 0.895) for deep WMH. Baseline prevalence of moderate-severe WMH (mean age 43.6 ± 9.5 years, 78% male) was 24.5%, much higher than in other, older healthy aging cohorts (Table). Age (OR 1.085, 95%CI 1.042-1.130), systolic blood pressure (OR 1.033, 95%CI 1.008-1.058) and regular injection drug use (OR 3.655, 95%CI 1.284-10.403) together explained 23.5% of variance in the presence of moderate-severe WMH within this population, with injection drug use having the largest effect. Conclusions: This young cohort appears to have an accelerated burden of cSVD, with injected drug use as a major risk factor. Further research is needed to elucidate potential mechanisms.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.012
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
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 head0.294
Teacher spread0.274 · 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
Published2017
Admission routes1
Has abstractyes

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