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The Effect of Repeated Zygapophysial Joint Radiofrequency Neurotomy on Pain, Disability, and Improvement Duration

2010· article· en· W2153726616 on OpenAlexaff
Brian Rambaransingh, Gordon Stanford, Robert Burnham

Bibliographic record

VenuePain Medicine · 2010
Typearticle
Languageen
FieldMedicine
TopicSpine and Intervertebral Disc Pathology
Canadian institutionsUniversity of AlbertaUniversity of Calgary
Fundersnot available
KeywordsMedicineNeurotomyStatistical significanceRepeated measures designLumbarNeck painChronic painPhysical therapyLow back painPain reliefAnesthesiaAnalysis of varianceSurgeryInternal medicine

Abstract

fetched live from OpenAlex

OBJECTIVE: To assess the effectiveness of repeated radiofrequency neurotomy (RFN) on pain, disability, and treatment effect duration. PATIENTS: One hundred-four patients who underwent repeat RFN for chronic neck or back pain were prospectively followed using a Pain Disability Questionnaire-Spine (PDQ-S). Complete data sets were available for 73, 73, and 36 patients for the 1st, 2nd, and 3rd RFN, respectively. RESULTS: Pain intensity, pain frequency, and patient-specific disability measures were significantly improved post-initial, second, and third RFN. Moreover, there was no statistically significant difference among the PDQ-S scores post-RFN 1, 2, and 3. There was no statistical significance between the duration of pain relief post-RFN 1 and pain relief post-RFD 2. CONCLUSION: Repeated cervical and lumbar RFN reduces pain and disability with equal effectiveness for approximately 10 months in patients with facetogenic chronic neck and back pain.

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0010.000

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 head0.009
GPT teacher head0.275
Teacher spread0.266 · 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 designObservational
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

Citations27
Published2010
Admission routes1
Has abstractyes

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