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Record W4406550471 · doi:10.1016/j.spinee.2025.01.003

Evaluating variability in decision-making among spine surgeons treating adult spine deformity

2025· article· en· W4406550471 on OpenAlexaff
Mark Abdelnour, Rohail Mumtaz, Mamdoh Al Hawsawi, Feras Qumqumji, Ganesh Swamy, Kenneth Thomas, Alex Soroceanu, Wang Zhi, Alexandra Stratton, Stephen Kingwell, Eugene K. Wai, Philippe Phan

Bibliographic record

VenueThe Spine Journal · 2025
Typearticle
Languageen
FieldMedicine
TopicScoliosis diagnosis and treatment
Canadian institutionsUniversité de MontréalOttawa HospitalWilfrid Laurier UniversityFoothills Medical CentreUniversity of Ottawa
FundersAOSpineStrykerMedtronic
KeywordsMedicineSPINE (molecular biology)Spinal deformityDeformityPhysical therapySurgeryBioinformatics

Abstract

fetched live from OpenAlex

BACKGROUND CONTEXT: Significant variability in the management of Adult Spinal Deformity (ASD) has been observed among spine surgeons worldwide. The variability among Canadian spine surgeons, a country with universal public healthcare, remains unknown. PURPOSE: The study aims to evaluate areas of variability in the perioperative optimization and surgical management of ASD among Canadian spine surgeons. STUDY DESIGN/SETTING: In this cross-sectional study, 25 Canadian spine surgeons, predominantly orthopedic surgeons (20) and neurosurgeons (5) with varying experience, participated in an online survey focused on Adult Spinal Deformity (ASD). SAMPLE: The study involved 25 Canadian spine surgeons with varying level of experience, representing both orthopedic and neurosurgical specialities. OUTCOME MEASURE: The study aimed to evaluate the variability in surgical decision-making and perioperative optimization strategies among Canadian spine surgeons when faced with simulated scenarios of ASD pathologies. METHODS: The online survey presented 4 vignettes with simulated scenarios of the most common ASD pathologies, including High Grade Spondylolisthesis (HGS), Neglected Adolescent Idiopathic Scoliosis (NAIS), Degenerative Scoliosis (DS), and Flat Back Syndrome (FBS). Questions in the vignettes explored ASD surgical decision-making, while additional questions focused on perioperative optimization. Descriptive statistics were used to analyze multiple-choice responses, and open-text responses were categorized into themes. RESULTS: Variability was observed in the duration conservative treatment across the 4 ASD cases. Surgeons exhibited variability in the use of preoperative osteoporosis treatment. There was varied use of a dedicated anesthesiology team. Surgical goals varied in HGS and NAIS. The primary surgical method was variable in DS and HGS, the type of osteotomy varied in DS and FBS, and level of fixation varied in HGS and NAIS. Consensus was observed in the use of intraoperative monitoring across of all 4 ASD pathologies, the implementation of a team-based approach, and the selection of the primary surgical goal in DS and FBS. CONCLUSION: Our cross-sectional study revealed variability among Canadian spine surgeons in the management of ASD, potentially influenced by the uncertain ASD progression, the need for evidence-based nonsurgical guidelines, and insufficient evidence on optimal surgical approaches. These findings will help guide future research to ultimately reduce variability and improve ASD patient management and outcomes.

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)0.027
metaresearch head score (Gemma)0.242
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 score0.027
Threshold uncertainty score0.140

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0270.242
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.384
Teacher spread0.357 · 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".

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Citations1
Published2025
Admission routes1
Has abstractno

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