MétaCan
Menu
Back to cohort
Record W2429150456 · doi:10.1017/cjn.2016.186

P.082 Traumatic inter hemispheric subdural hematomas – clinical presentation, management and outcome

2016· article· en· W2429150456 on OpenAlexvenueno aff
R Bokari, Solon Schur, Céline Couturier, Ahmed AlAzri, Judith Marcoux, Mohammad Reza Maleki

Bibliographic record

VenueCanadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques · 2016
Typearticle
Languageen
FieldMedicine
TopicNeurosurgical Procedures and Complications
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineSubdural HematomasPresentation (obstetrics)Conservative managementHematomaIncidence (geometry)Acute subdural hematomaSubarachnoid hemorrhageTraumatic brain injurySurgery

Abstract

fetched live from OpenAlex

Background: There is currently little data on the incidence, clinical outcome and management of traumatic interhemispheric subdural hematomas (IHSDHs). Methods: All patients admitted with an acute subdural hematoma (SDH) over a 5-year period at a Level I trauma center were included. A detailed review of all cases of large IHSDH (≥7 mm) was performed to document clinical presentation, management and outcomes. Results: Of 1182 patients with acute subdural hematomas (SDHs), 420 had IHSDHs (24%), and 50 were large IHSDHs. For patients with large IHSDH, the average age was 76 years (±11) and 44% were female. The average GCS was 12 on presentation (±4), and the average GOSE was 4 (±2). 66% of patients had associated cranial/ intracranial injuries (fracture, subarachnoid/epidural/SDH) and 26% required operations for acute convexity SDH. Three patients required operations for their IHSDH by inter hemispheric approach. By 10 weeks, 82% had a complete resolution of the IHSDHs. Conclusions: IHSDHs are often referred to as rare entities. Our results show they are common. Conservative management is often appropriate to manage even large IHSDHs, as most resolve spontaneously. This study will help document the occurrence of falx syndrome, as well as the management and outcomes of larger IHSDHs.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.083
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.005
Scholarly communication0.0000.001
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 head0.081
GPT teacher head0.352
Teacher spread0.271 · 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.

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
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueCanadian Journal of Neurological Sciences / Journal Canadien des Sciences NeurologiquesSame topicNeurosurgical Procedures and ComplicationsFrench-language works237,207