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Record W4396760958 · doi:10.1016/j.lana.2024.100760

Traumatic injury to the posterior fossa: a secondary analysis and description of case series from the NEXUS head injury dataset

2024· article· en· W4396760958 on OpenAlexaboutno aff
Richelle J. Cooper, Thomas E. Akie, Tarika Gujral, Shivam Rana, Kyle Bui, Ryan Factora, Alexandra Quinones, Gregory W. Hendey, Robert M. Rodriguez, William R. Mower

Bibliographic record

VenueThe Lancet Regional Health - Americas · 2024
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury and Neurovascular Disturbances
Canadian institutionsnot available
FundersNational Institute of Neurological Disorders and StrokeNational Heart, Lung, and Blood InstituteNational Institutes of HealthCenters for Disease Control and PreventionPatient-Centered Outcomes Research Institute
KeywordsNexus (standard)Head injurySeries (stratigraphy)Head (geology)MedicineComputer scienceSurgeryGeologyPaleontology

Abstract

fetched live from OpenAlex

Background: Traumatic brain injuries involving the posterior fossa are rare and case reports indicate they often result in severe outcomes. We seek to describe characteristics and outcomes of traumatic posterior fossa injuries. Methods: We performed a planned secondary analysis of all patients with posterior fossa injuries enrolled in the NEXUS head computed tomography (CT) validation study dataset. The dataset includes prospectively collected data on all patients undergoing non-contrast cranial CT following blunt traumatic head injury from April 2006 to December 2015, at four emergency departments comprising community and university sites, as well as urban, suburban and rural settings in California (Antelope Valley Hospital, San Francisco General Hospital, UCLA Ronald Reagan Medical Center, UCSF Fresno Community Regional Medical Center). We classified each patient into one of three injury patterns: Type I-notable traumatic injuries primarily above the tentorium, with minimal posterior fossa involvement; Type II-notable traumatic injuries both above and within the posterior fossa; and Type III-notable traumatic injuries primarily within the posterior fossa. We extracted demographic data for each patient as well as physician assessments of the NEXUS head CT and Canadian Head CT rule clinical criteria, mechanisms of injury, patient outcomes, and the location and types of intracranial injuries sustained. Findings: Of 11,770 patients in the database, 184 (1.6%) had posterior fossa injuries on CT imaging. Mean age was 55.4 years (standard deviation 22.5 years, range 2-96 years); 131 (71.2%) were males. We identified 63 patients with Type I injuries, 87 with Type II injuries, and 34 Type III injuries. The most common mechanisms of injury were falls (41%), pedestrian vs automobile (15%), and motor vehicle collisions (13%). On presentation most patients had altered mental status (72%), abnormal behavior (53%), or a neurologic deficit (55%). The majority of individuals, 151 (82%), had clinically important injuries and 111 (60%) required neurosurgical intervention. The dispositions for the subjects included 52 deaths (28%), 49 (27%) patients discharged home, and 48 (26%) discharged to rehabilitation facilities. When compared to individuals with Type I and Type II injuries, patients with Type III injuries had lower mortality (6% vs 30% and 35%) and higher percentage of patients discharged home (60% vs 19% and 21%). Interpretation: Patients with Type I and II injury patterns (those that involve both the posterior fossa and supratentorium) experienced high mortality and disability. Patients with Type III injuries (isolated posterior fossa) had a better prognosis. Funding: None.

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)0.004
metaresearch head score (Gemma)0.015
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 score0.009
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.015
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0060.005
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0020.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.105
GPT teacher head0.377
Teacher spread0.272 · 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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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