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Record W4412671502 · doi:10.3171/2025.3.jns242811

Operative neurosurgery for traumatic subdural hematoma: association between trauma center variation and patient outcomes

2025· article· en· W4412671502 on OpenAlexaff
Vikas N. Vattipally, Kathleen R. Ran, Debraj Mukherjee, José I. Suárez, Judy Huang, Chetan Bettegowda, Elliott R. Haut, Joseph V. Sakran, Christopher D. Witiw, David Gómez, Tej D. Azad, James P. Byrne

Bibliographic record

VenueJournal of neurosurgery · 2025
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury and Neurovascular Disturbances
Canadian institutionsUniversity of British ColumbiaSt. Michael's Hospital
Fundersnot available
KeywordsMedicineGlasgow Coma ScaleNeurosurgeryTraumatic brain injuryTrauma centerHematomaInjury Severity ScoreOdds ratioRetrospective cohort studyCohortLogistic regressionEmergency medicineSurgeryPoison controlInjury preventionInternal medicinePsychiatry

Abstract

fetched live from OpenAlex

OBJECTIVE: Traumatic subdural hematoma (SDH) is a common form of traumatic brain injury (TBI) that often represents a neurosurgical emergency. Surgical evacuation is recommended for SDH with midline shift (MLS) > 5 mm, regardless of the presenting Glasgow Coma Scale (GCS) score; however, real-world practice is unknown. The objective of this study was to test the hypothesis that significant variation exists in the tendency for operative neurosurgical intervention for traumatic SDH among trauma centers (TCs) and that this variation is associated with patient outcomes. METHODS: The authors performed a retrospective cohort study of adult patients (age ≥ 18 years) treated for blunt severe TBI (GCS score ≤ 8) and SDH with MLS > 5 mm at level I and II TCs participating in the American College of Surgeons Trauma Quality Improvement Program (2017-2019). Patients with nonsurvivable injuries (Abbreviated Injury Scale score 6), advance directives, or emergency department death were excluded. Hierarchical logistic regression was used to estimate each TC's unique odds of performing operative neurosurgery for traumatic SDH. Risk adjustment accounted for patient baseline and injury characteristics. TCs were grouped into quartiles of increasing tendency for neurosurgery. The risk-adjusted association between TC tendency for operative neurosurgery and outcomes was then measured. The primary outcome was inpatient mortality. The secondary outcome was favorable discharge, defined as discharge to home or rehabilitation. RESULTS: The authors identified 13,087 patients with traumatic SDH treated at 454 level I and II TCs. Significant variation in TC tendency for operative neurosurgery was observed. Specifically, TCs with the greatest tendency for neurosurgical intervention (quartile 4) performed surgery on 60% of patients, whereas TCs with the lowest tendency (quartile 1) performed surgery on only 26%, even though there were no differences in GCS scores or pupillary examination findings. After risk adjustment, a greater hospital tendency for neurosurgical intervention was associated with lower inpatient mortality and higher odds of favorable discharge. Patients with traumatic SDH treated at TCs with the highest versus the lowest tendency for neurosurgery were 30% less likely to die (adjusted odds ratio [aOR] 0.7, 95% CI 0.6-0.8) and more likely to have a favorable discharge (aOR 1.3, 95% CI 1.1-1.6). These effects were most pronounced among patients with abnormal pupillary examination findings. CONCLUSIONS: Significant variation exists between trauma centers in performing operative neurosurgery for traumatic SDH. The TCs more likely to perform surgery were associated with lower odds of inpatient mortality and higher odds of a favorable discharge. Consensus-based guidelines are needed to improve standardization in the care of patients with traumatic SDH.

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)0.001
metaresearch head score (Gemma)0.006
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 score0.005
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.026
GPT teacher head0.297
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 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

Citations3
Published2025
Admission routes1
Has abstractyes

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