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Record W4379279878 · doi:10.1017/cjn.2023.97

E.3 Imaging of subdural hematoma

2023· article· en· W4379279878 on OpenAlexaffvenue
Sandeep Devgan, J Shankar

Bibliographic record

VenueCanadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques · 2023
Typearticle
Languageen
FieldMedicine
TopicNeurosurgical Procedures and Complications
Canadian institutionsUniversity of Winnipeg
Fundersnot available
KeywordsMiddle meningeal arteryMedicineHematomaRadiologyChronic subdural hematomaEmbolizationMagnetic resonance imagingMiddle cerebral arteryIschemiaInternal medicine

Abstract

fetched live from OpenAlex

Background: Radiologic imaging has become integral in not only the detection and diagnosis of subdural hematoma, but also in guiding potential treatment options. Particularly, in the arena of chronic subdural hematoma, which has conventionally been managed via surgical drainage, although is shifting toward procedural intervention with embolization of the middle meningeal artery. This paper aims to review the imaging manifestations of subdural hematoma as a function of chronicity, standardized methods of measurement, and identifying the middle meningeal artery and its clinically significant variant anatomy as it pertains to embolization planning. Methods: A literature search using key terms and titles was conducted for articles containing imaging characteristics of subdural hematoma, approaches to measurement, and middle meningeal artery anatomy as the primary focus. Results: The expected evolution of subdural hematoma over time encompasses a broad array of imaging characteristics. Attempts at standardizing hematoma measurements include width, volume, and midline shift. Given the implication of the middle meningeal artery in potential therapeutic embolization, familiarity with its anatomy is vital not only for mapping access, but also for delineating possible dangerous collaterals. Conclusions: Equipped with a more comprehensive approach to characterizing subdural hematoma, the radiologist will be able to curate findings of greater utility to the clinician.

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)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.185
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0010.005
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.043
GPT teacher head0.300
Teacher spread0.257 · 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; both teacher heads agree on what is shown here.

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

Citations0
Published2023
Admission routes2
Has abstractyes

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