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Record W2023549771 · doi:10.1177/0333102414534082

Occipital nerve stimulation for chronic migraine: A randomized trial on subthreshold stimulation

2014· article· en· W2023549771 on OpenAlexaboutno aff
PJ Slotty, Gregor Bara, L Kowatz, Astrid Gendolla, Christian Wille, Stefan Schu, Jan Vesper

Bibliographic record

VenueCephalalgia · 2014
Typearticle
Languageen
FieldMedicine
TopicMigraine and Headache Studies
Canadian institutionsnot available
Fundersnot available
KeywordsStimulationMedicineVisual analogue scaleAnesthesiaSubthreshold conductionMigraineRandomized controlled trialPlaceboOccipital nerve stimulationSurgeryInternal medicine

Abstract

fetched live from OpenAlex

INTRODUCTION: Occipital nerve stimulation (ONS) may provide pain relief in migraine patients. In this double-blinded trial we investigated the significance of paresthesia and possible placebo effects. METHODS: Patients already treated with ONS reporting stable treatment effect were included. "Effective stimulation," "subthreshold stimulation" and "no stimulation" were compared. Patients cycled through all three treatment groups. Outcome was measured using the visual analog scale (VAS) for pain, McGill Pain Questionnaire and SF-36. RESULTS: Eight patients were included, mean preoperative VAS was 8.20 ± 1.22. A significant improvement in pain was observed in favor of suprathreshold stimulation compared to subthreshold stimulation (1.98 ± 1.56 vs 5.65 ± 2.11). Pain also significantly improved under subthreshold stimulation compared to no stimulation (5.65 ± 2.11 vs 8.45 ± 0.99). No changes in SF-36 were observed. CONCLUSIONS: Paresthesia is not required to achieve pain reduction but suprathreshold stimulation yields better results, underlining the significance of stimulation parameter customization.

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)0.002
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.024
GPT teacher head0.314
Teacher spread0.289 · 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 designRandomized trial
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

Citations49
Published2014
Admission routes1
Has abstractyes

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