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Cervical Spine Loading Characteristics in a Cadaveric C5 Corpectomy Model Using a Static and Dynamic Plate

2004· article· en· W1973332132 on OpenAlexaff
D. Reidy, Joel Finkelstein, A. Nagpurkar, Payam Mousavi, Cari Whyne

Bibliographic record

VenueJournal of Spinal Disorders · 2004
Typearticle
Languageen
FieldMedicine
TopicCervical and Thoracic Myelopathy
Canadian institutionsWomen's College HospitalSunnybrook Health Science Centre
Fundersnot available
KeywordsCorpectomyCadaveric spasmMedicineBiomechanicsCadaverSpinal fusionSurgeryCervical spineAnatomy

Abstract

fetched live from OpenAlex

Anterior plates are used to increase the initial stability of anterior cervical spine fusions; however, plating has been suggested to cause graft stress shielding, leading to reduced fusion rates. The objectives of this study were to quantify the effects of graft size and plating (static versus dynamic) and the role of the posterior elements on load transmission in anterior cervical fusion. A C5 corpectomy was performed on six human cervical spines (C3-C7). An instrumented height-adjustable graft and dynamic cervical plate were used to measure axial load transmission. Each specimen underwent axial compressive testing with dynamic and static plate configurations, optimal and undersized graft heights, and posterior elements intact and removed. Dynamic plating allowed significantly more load transmission by the graft, particularly in the undersized graft configuration. The posterior elements play a significant role in load transmission.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
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 head0.016
GPT teacher head0.299
Teacher spread0.283 · 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 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

Citations46
Published2004
Admission routes1
Has abstractyes

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