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Record W2045560992 · doi:10.3727/036012908803861177

Acupuncture of Specific Points Influences Cortical Auditory Evoked Potentials – A Volunteer Crossover Study

2008· article· en· W2045560992 on OpenAlexaff
Peggy Lietz, Reinhard Schmidt, Werner Hosemann, Dragan Pavlović, Vasyl Gizhko, Christine Lehmann, Michael Wendt, Taras Usichenko

Bibliographic record

VenueAcupuncture & Electro-Therapeutics Research · 2008
Typearticle
Languageen
FieldMedicine
TopicAcupuncture Treatment Research Studies
Canadian institutionsDalhousie University
Fundersnot available
KeywordsAcupunctureVolunteerStimulationAudiologyCrossover studyMedicinePsychologyPhysical medicine and rehabilitationInternal medicinePathologyAlternative medicine

Abstract

fetched live from OpenAlex

Specificity of acupoints remains a crucial question in acupuncture research. The aim was to investigate whether acupuncture of specific points influences the Cortical Auditory Evoked Potentials (CAEP). Ten healthy volunteers were enrolled in this study according to inclusion criteria. One of 4 acupoints: TH3, GB43 (both claimed as specific for auditory system by Traditional Chinese Medicine) and non-specific points H7 and ST44, was stimulated during one session. Each volunteer received 4 sessions of acupuncture with an interval of 1 week between the sessions. The latencies and amplitudes of CAEP were registered before and after the acupuncture during each session. The mean peak latencies of P2 component decreased after stimulation of TH3 by 11 ms and GB43 by 14 ms whereas the peak latencies of N2 component increased after stimulation of TH3 by 9 ms and GB43 by 4 ms compared to baseline values (p<0.05). The stimulation of H7 and ST44 did not produce any changes. These findings confirm the specificity of acupuncture points TH3 and GB43 in relation to auditory system.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.126
GPT teacher head0.426
Teacher spread0.300 · 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 designNon-randomized 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

Citations3
Published2008
Admission routes1
Has abstractyes

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