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Record W4390631636 · doi:10.1136/rapm-2023-105152

Parasagittal needle placement approach for lumbar medial branch denervation: a brief technical report

2024· article· en· W4390631636 on OpenAlexaff
John Tran, Arden Lawson, Anne Agur, Eldon Loh

Bibliographic record

VenueRegional Anesthesia & Pain Medicine · 2024
Typearticle
Languageen
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsLawson Health Research InstituteUniversity of TorontoWestern University
Fundersnot available
KeywordsMedicineDenervationLumbarAnatomyLumbar NerveProcess (computing)Surgery

Abstract

fetched live from OpenAlex

Radiofrequency denervation of lumbar medial branches is a viable treatment option to manage chronic facetogenic low back pain. Traditionally, lumbar medial branch denervation involves placement of the electrode's active tip at a 20-degree angulation away from the parasagittal plane. However, more recent anatomical studies have provided evidence supporting the feasibility of an alternative parasagittal approach targeting the posterior half of the lateral neck of the superior articular process to capture the lumbar medial branches. Currently, there is a lack of clinical data on the effectiveness of the alternative parasagittal needle placement technique. Therefore, in this brief technical report, the parasagittal needle placement technique and the pain relief outcomes in four consecutive patients following treatment with the parasagittal approach are described.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0030.004

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 head0.026
GPT teacher head0.302
Teacher spread0.276 · 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 designCase report
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

Citations7
Published2024
Admission routes1
Has abstractyes

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