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Record W3164884583

Could vagus nerve stimulation influence bone remodeling?

2021· article· en· W3164884583 on OpenAlexaff
Malik E. Juweid, Abdelkarim A. Al-Qudah, Al Masri A, Said Dahbour, Al Bahou Y, Ahmad W. Al Shareef, Iskandar Tamimi

Bibliographic record

VenuePubMed · 2021
Typearticle
Languageen
FieldNeuroscience
TopicVagus Nerve Stimulation Research
Canadian institutionsMcGill University
Fundersnot available
KeywordsMedicineVagus nerve stimulationBone mineralOsteoporosisLumbarVagus nerveProspective cohort studyUrologySurgeryRefractory (planetary science)StimulationAnesthesiaInternal medicine
DOInot available

Abstract

fetched live from OpenAlex

OBJECTIVES: To investigate the effect of vagus nerve stimulation (VNS) on the bone mineral density (BMD) in epileptic patients. METHODS: A prospective cohort study was conducted on individuals with refractory seizures who underwent VNS surgery between January 2012 and December 2018. BMD was measured preoperatively and between 6 months and one year after surgery. RESULTS: Twenty-one patients (mean age (±SD)=23.6±12.3 years) were recruited for the implantation of a VNS device. The mean absolute increase in lumbar BMD in the 21 patients was 0.04±0.04 g/cm2 resulting in an overall percent increase from baseline of 4.7±6.1%. BMD increased by an amount ≥ the least significant change (LSC) for the lumbar spine in 13 patients (61.9%). The lumbar Z score also increased in these patients from -1.22±1.15 to -0.88±1.22, P=0.006). Pre and Post VNA femoral BMD was measured in only 11 patients and, of those 3 showed a significant increase in BMD, 1 a significant decrease and 7 no change. CONCLUSION: The implantation of a VNS was associated with an increase in lumbar BMD. This study could lead to a new application for VNS in the treatment of osteoporosis.

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)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.559
Threshold uncertainty score0.641

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.005
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 integrity0.0000.000
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 head0.085
GPT teacher head0.312
Teacher spread0.227 · 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 designSimulation or modeling
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

Citations6
Published2021
Admission routes1
Has abstractyes

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