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Record W2294694312 · doi:10.1093/pm/pnv050

Investigating the Effects of Pulsed Radiofrequency on Dorsal Root Ganglion in Chronic Lumbar Radicular Pain Patients: Is It Not Important that We Ask the Right Question, the Right Way, on an Appropriate Sample of Patients?

2015· letter· en· W2294694312 on OpenAlexaff
Harsha Shanthanna

Bibliographic record

VenuePain Medicine · 2015
Typeletter
Languageen
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsSt. Joseph’s Healthcare HamiltonMcMaster UniversitySt. Joseph's Hospital
Fundersnot available
KeywordsMedicineNeurogenic claudicationRadicular painPulsed radiofrequencySpinal stenosisLumbar spinal stenosisNerve rootLumbarStenosisSciaticaSpondylolisthesisLow back painLumbosacral jointLateral recessSpinal canal stenosisDorsal root ganglionBack painRadiologySurgeryDorsumSpinal canalAnatomyPain reliefPathologySpinal cord

Abstract

fetched live from OpenAlex

Dear Editor, It is interesting to read the paper by Koh et al. on the investigation of the pulsed radiofrequency (PRF) of the dorsal root ganglion (DRG) in patients of chronic lumbosacral radicular (CLR) pain [1]. It is noteworthy that the authors attempted to perform a controlled trial on a challenging topic. However, I am afraid that the study design, results, and conclusions have further “muddied the water” instead of bringing clarity to the existing evidence on the efficacy of PRF-DRG in CLR pain patients. I would like to highlight some aspects of the study, which decrease the confidence in their study results and conclusions. The study was conducted on patients of lumbar spinal stenosis (LSS). Note that LSS is a radiological diagnosis, and the clinical diagnosis of spinal stenosis and its symptoms (leg pain) bear no relation to the extent of corresponding imaging findings of either central or foraminal stenosis. However, generally patients with central stenosis present with neurogenic claudication, whereas lateral foraminal stenosis could cause symptoms similar to radicular elements of …

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)0.017
metaresearch head score (Gemma)0.016
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.429
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0170.016
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.002
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 head0.011
GPT teacher head0.258
Teacher spread0.247 · 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.

Study designNot applicable
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

Citations3
Published2015
Admission routes1
Has abstractyes

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