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Record W4412121534 · doi:10.1177/21925682241264221

Identification and Management of Neurologic Complications in Patients Undergoing Adult Spinal Deformity Surgery

2025· article· en· W4412121534 on OpenAlexaff
Christopher J. Nielsen, Justin S. Smith, Allan R. Martin, Brett Rocos, Thorsten Jentzsch, Robert Ravinsky, Colby Oitment, Markian Pahuta, So Kato, Stephen J. Lewis

Bibliographic record

VenueGlobal Spine Journal · 2025
Typearticle
Languageen
FieldMedicine
TopicSpinal Fractures and Fixation Techniques
Canadian institutionsMcMaster UniversityUniversity Health NetworkUniversity of TorontoHamilton Health SciencesImpactHamilton General HospitalToronto Western Hospital
Fundersnot available
KeywordsMedicinePerioperativeComplicationSurgeryDeformitySystematic reviewMEDLINEIntensive care medicine

Abstract

fetched live from OpenAlex

Study DesignNarrative Literature Review.ObjectiveTo provide a comprehensive literature review of neurologic complications in Adult Spinal Deformity (ASD) surgery in the pre-operative, peri-operative and post-operative periods.MethodsA broad review of the literature was conducted using the multiple databases including Pubmed, Embase, Scopus and the Cochrane library. Individual studies of relevance were appraised and included at the discretion of the authors on the basis of pertinence, impact on practice and scientific merit.ResultsThe evidence regarding epidemiology, classification of complications, pre-operative evaluation of patients, peri-operative strategies to mitigate risk, intra-operative management of neuromonitoring changes and post-operative management of neurologic injury was critically appraised. Patients with the highest risk of neurologic complication include those with pre-surgery neuroaxis abnormality, high Deformity Angular Ratio, 3 column osteotomies and increased blood loss. Accurate and timely identification of intraoperative neuromonitoring changes is critical to ensuring appropriate response depending on whether changes are perfusion based (maintain adequate MAP and Hb, reverse corrective maneuvers) or traumatic (decompression of neural elements, assessment of instrumentation, reversal of corrective maneuvers, steroids). Surgical checklists can help surgeons navigate these stressful events to ensure appropriate steps and interventions are taken.ConclusionNeurological injuries occurring during the course of ASD surgery are potentially devastating complication, with regards to both patient morbidity and economic impact. Pre-operative identification of high risk patients, perioperative strategies to improve safety, timely recognition and management of intra-operative neuromonitoring changes and post-operative supportive measures can potentially reduce the incidence and significance of neurological injuries.

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 score0.072
Threshold uncertainty score0.235

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 head0.013
GPT teacher head0.305
Teacher spread0.292 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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