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Record W4416921629 · doi:10.1161/svi270000_474

Abstract 474: The Prevalence and Evolution of Cognitive Deficits in Chronic Subdural Hematoma Patients

2025· article· en· W4416921629 on OpenAlexaboutno aff
A. Letavay, Elliot Pressman, Vikalpa Dammavalam, Nilám Ram, S Amin, Kunal Vakharia, Waldo R. Guerrero, Maxim Mokin

Bibliographic record

VenueStroke Vascular and Interventional Neurology · 2025
Typearticle
Languageen
FieldMedicine
TopicIntracerebral and Subarachnoid Hemorrhage Research
Canadian institutionsnot available
Fundersnot available
KeywordsCognitionIncidence (geometry)Chronic subdural hematomaHematomaCognitive Assessment SystemIntervention (counseling)Effects of sleep deprivation on cognitive performance

Abstract

fetched live from OpenAlex

Introduction / Purpose Chronic subdural hematomas (cSDH) represent the most common form of intracranial hemorrhage in the elderly, with an estimated incidence ranging from 2 to 21 cases per 100,000 individuals. Although the incidence has increased over the past decade, the specific effects of cSDH on various domains of cognitive function remain inadequately characterized. In light of these gaps in the literature, the present study was aimed to assess cognitive performance of patients with cSDH at two distinct time points during treatment to better delineate the trajectory of cognitive outcomes in this population. Materials / Methods Patients with a diagnosis of cSDH treated at our institution were enrolled and prospectively followed during the study period from August 2024 until August of 2025. We successfully administered the Montreal Cognitive Assessment (MoCA) and the Controlled Oral Word Association (COWA) in 27 patients prior to the initiation of standard of care procedures. Of these patients, 5 underwent the examination at two time points: before undergoing a standard of care intervention (craniotomy, MMA embolization, medication management) and at 3 ‐ 6 months (+/ ‐ 3 weeks) following the date of treatment. In addition, 7 more patients were enrolled, and one assessment was performed with a second one expected to be completed at the time of conference presentation. Additional information such as thickness of the hematoma, information regarding baseline level of cognitive functioning, and radiographic appearance of the hematoma were also collected. Results One assessment was successfully completed in 27 patients. 5 patients completed the assessments at both time points. In addition, 7 more patients were enrolled, with a second assessment expected to be completed at the time of conference presentation. Scores on the MoCA assessment prior to the initiation of standard of care procedures ranged from 5/30 to 28/30, with the average score being an 8/30. Of those who have completed a follow up assessment, the average score on the MoCA increased by 7.5 points, respectively. Interestingly, the same trend was not observed for scores on the COWA at both time points. The average length of time between each assessment was 3.5 months (+/‐ 3 weeks). GCS scores increased in those with clinical improvement. No interruptions to standard of care workflows were noted. Correlations between changes in assessment scores and appearance of the hematoma at 3‐6 months are being finalized and prepared for the SVIN conference. Conclusions Preliminary analyses have demonstrated that diminished size and resolution of the hematoma is associated with higher scores on the MoCA and the COWA in some patients, mirroring findings in the literature. However, in some patients, MoCA scores decreased despite decreased hematoma volume, which demonstrates the complexity of measuring cognitive function in this population. No standard of care workflow interruptions were noted.

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)0.002
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 score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.009
GPT teacher head0.276
Teacher spread0.267 · 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
Published2025
Admission routes1
Has abstractyes

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