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Record W3120806230 · doi:10.2147/jpr.s255726

A Literature Review of Dorsal Root Entry Zone Complex (DREZC) Lesions: Integration of Translational Data for an Evolution to More Accurate Nomenclature

2021· review· en· W3120806230 on OpenAlexaff
Ognjen Visnjevac, Frederick Ma, Alaa Abd‐Elsayed

Bibliographic record

VenueJournal of Pain Research · 2021
Typereview
Languageen
FieldMedicine
TopicSpine and Intervertebral Disc Pathology
Canadian institutionsMcMaster UniversitySTART Clinic
Fundersnot available
KeywordsMedicineDorsumLesionFluoroscopyNeuroscienceSensory systemAnatomyPathologyRadiologyBiology

Abstract

fetched live from OpenAlex

The purpose of this translational review was to provide evidence to support the natural evolution of the nomenclature of neuromodulatory and neuroablative radiofrequency lesions for pain management from lesions of individualized components of the linear dorsal afferent pathway to "Dorsal Root Entry Zone Complex (DREZC) lesions." Literature review was performed to collate anatomic and procedural data and correlate these data to clinical outcomes. There is ample evidence that the individual components of the DREZC (the dorsal rami and its branches, the dorsal root ganglia, the dorsal rootlets, and the dorsal root entry zone) vary dramatically between vertebral levels and individual patients. Procedurally, fluoroscopy, the most commonly utilized technology is a 2-dimensional x-ray-based technology without the ability to accurately locate any one component of the DREZC dorsal afferent pathway, which results in clinical inaccuracies when naming each lesion. Despite the inherent anatomic variability and these procedural limitations, the expected poor clinical outcomes that might follow such nomenclature inaccuracies have not been shown to be prominent, likely because these are all lesions of the same anatomically linear sensory pathway, the DREZC, whereby a lesion in any one part of the pathway would be expected to interrupt sensory transmission of pain to all subsequent more proximal segments. Given that the common clinically available tools (fluoroscopy) are inaccurate to localize each component of the DREZC, it would be inappropriate to continue to erroneously refer to these lesions as lesions of individual components, when the more accurate "DREZC lesions" designation can be utilized. Hence, to avoid inaccuracies in nomenclature and until more accurate imaging technology is commonly utilized, the evidence herein supports the proposed change to this more sensitive and inclusive nomenclature, "DREZC lesions."

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)0.003
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.011
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.013
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0110.010
Science and technology studies0.0010.001
Scholarly communication0.0020.003
Open science0.0020.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.412
GPT teacher head0.554
Teacher spread0.142 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations7
Published2021
Admission routes1
Has abstractyes

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