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Acute Histologic Effects and Thermal Distribution Profile of Disc Biacuplasty Using a Novel Water-Cooled Bipolar Electrode System in an<i>In vivo</i>Porcine Model

2007· article· en· W1935099951 on OpenAlexaff
Jeffrey D. Petersohn, Laura R. Conquergood

Bibliographic record

VenuePain Medicine · 2007
Typearticle
Languageen
FieldMedicine
TopicSpine and Intervertebral Disc Pathology
Canadian institutionsBaylis Medical (Canada)
Fundersnot available
KeywordsAnnulus (botany)Intervertebral discLumbarIn vivoMedicineLow back painBiomedical engineeringIntervertebral diskIntervertebral Disc DisplacementNerve rootAnatomyLumbar vertebraeMaterials sciencePathologyComposite materialBiology

Abstract

fetched live from OpenAlex

BACKGROUND: Thermal treatment of the lumbar intervertebral disc has been suggested for the treatment of chronic discogenic pain. Disc biacuplasty (D-BAC) is a novel procedure that uses two water-cooled radiofrequency electrodes in a bipolar configuration to heat a large volume of the posterior annulus fibrosus. METHODS: Seven porcine lumbar discs were treated with D-BAC to assess acute effects on the treated tissue in a "worst-case"in vivo model. Intradiscal and peridiscal temperatures were measured during treatment and histologic analysis was used to assess for evidence of acute thermal injury. RESULTS: Temperature monitoring at designated safety zones outside the disc demonstrated maintenance of near-physiologic conditions while temperature in the inner posterior annulus reached 65 degrees C. Histologic sections of treated discs demonstrated no evidence of thermal damage to the dorsal root ganglia or spinal nerve roots when compared with controls. Increased coarseness of the fibrillar matrix and loss of cellular detail were noted in the nucleus pulposus of treated discs. DISCUSSION: Disc biacuplasty, in a porcine model, achieves suitable temperatures to induce thermal transition of collagen and thermoneurolysis while showing no evidence of damage to neural tissue in safety zones surrounding the disc.

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)0.002
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 score0.676
Threshold uncertainty score0.626

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.017
GPT teacher head0.283
Teacher spread0.267 · 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

Citations37
Published2007
Admission routes1
Has abstractyes

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