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Record W4403886581 · doi:10.1177/11795735241292185

Measurement of cerebral venous oxygenation with quantitative susceptibility mapping after subarachnoid hemorrhage: A pilot study

2024· article· en· W4403886581 on OpenAlexaboutno aff
Meng Qi, Lei Zhang, Ning Wang, Lidan Jiang, Hao Zhao, Wenjin Chen, Yaqian Xu

Bibliographic record

VenueJournal of Central Nervous System Disease · 2024
Typearticle
Languageen
FieldMedicine
TopicIntracranial Aneurysms: Treatment and Complications
Canadian institutionsnot available
FundersNational Natural Science Foundation of ChinaNational Science Foundation
KeywordsSubarachnoid hemorrhageOxygenationMedicineAnesthesiaCardiology

Abstract

fetched live from OpenAlex

Objective We measured cerebral venous oxygenation after aneurysmal subarachnoid hemorrhage (aSAH) using quantitative susceptibility mapping (QSM) to explore its relationship with cognitive function. Methods Twenty participants, including 10 patients with aSAH and 10 healthy volunteers as the control group, were included. Patients with aSAH were evaluated at 2 days, 3 weeks, and 6 months after aSAH. Each participant underwent magnetic resonance imaging and completed the Montreal Cognitive Assessment (MoCA) at baseline, midpoint, and endpoint. QSM was used to determine the magnetic susceptibility of the cerebral veins. Furthermore, the relationship between MoCA and oxygen saturation in the cerebral veins was examined. Results The first scans of the cerebral veins and straight sinus susceptibility were considerably more significant in the aSAH group than in the healthy control group. At the 6-month follow-up, the mean oxygen saturation steadily increased in the aSAH group. Cerebral venous oxygen saturation was moderately correlated with MoCA (r = 0.5319, P = .0025). Conclusion QSM can be used to measure changes in cerebral venous oxygenation levels in patients with aSAH. During the acute phase of aSAH, there is a reduction in the oxygen saturation in the cerebral veins, and the shift in oxygen saturation levels may correlate with cognitive outcomes in patients with aSAH.

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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.205
Threshold uncertainty score0.551

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.028
GPT teacher head0.258
Teacher spread0.230 · 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 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".

Quick stats

Citations1
Published2024
Admission routes1
Has abstractyes

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