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Record W4415816334 · doi:10.1115/1.4070288

How Spinal Flexion Influences Fracture Morphology in the Porcine Spine

2025· article· en· W4415816334 on OpenAlexafffund
Safiya Abuani, Sofia Platnick, Noah Chow, Sabrina I. Sinopoli, Diane E. Gregory

Bibliographic record

VenueJournal of Biomechanical Engineering · 2025
Typearticle
Languageen
FieldMedicine
TopicCervical and Thoracic Myelopathy
Canadian institutionsWilfrid Laurier University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsFracture (geology)Cervical spineSpinal fractureCervical vertebraeSPINE (molecular biology)BiomechanicsIntervertebral disc

Abstract

fetched live from OpenAlex

Acute injuries to the spine, including slips and falls and motor vehicle accidents, most commonly occur when the spine is positioned in flexion. Therefore, the purpose of this study was to investigate how spine position influences the morphology of vertebral fractures following rapid intervertebral disc pressurization. To investigate the effects of position on fracture morphology, 19 functional spine units (FSUs) (ten C3/4 and nine C5/6 specimens) dissected from porcine cervical spines underwent a rapid pressurization protocol. In this protocol, specimens were randomly assigned to undergo the protocol either in a neutral condition or in a 15 deg flexed condition. The rapid pressurization resulted in endplate fracture in eight of the ten specimens in the flexed condition and eight of the nine specimens in the neutral condition. When neutral and flexed specimens were compared, peak pressure, peak force, rate of pressurization, and size of fracture were not statistically different; however, flexed specimens had a significantly faster rate of depressurization, as well as a more anterior location of the fracture on the endplate. The findings of this study indicate that spine position, and specifically flexion, impacts fracture location in the anterior-posterior direction but does not appear to affect fracture likelihood or severity.

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)0.001
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.559
Threshold uncertainty score0.274

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.012
GPT teacher head0.284
Teacher spread0.273 · 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 designBench or experimental
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 routes2
Has abstractyes

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