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Record W2004998424 · doi:10.1155/2013/792383

Duration of Analgesia Induced by Acupuncture-Like TENS on Experimental Heat Pain

2013· article· en· W2004998424 on OpenAlexafffund
Yannick Tousignant‐Laflamme, Marilyne Brochu, Cynthia Dupuis-Michaud, Catherine Pagé, Draga Popovic, Marie-Eve Simard

Bibliographic record

VenueISRN Pain · 2013
Typearticle
Languageen
FieldMedicine
TopicAcupuncture Treatment Research Studies
Canadian institutionsCentre Hospitalier Universitaire de SherbrookeUniversité de Sherbrooke
FundersUniversité de Sherbrooke
KeywordsMedicineAnesthesiaAcupunctureDuration (music)

Abstract

fetched live from OpenAlex

Background. Acupuncture-like TENS (AL-TENS) is a treatment modality that can be used to temporarily reduce pain. However, there is no clear data in the literature regarding the specific duration of analgesia induced by AL-TENS. Objectives. To describe and quantify the duration and magnitude of AL-TENS analgesia on experimental heat pain in healthy subjects and verify if the duration or magnitude of analgesia induced by the AL-TENS was influenced by the duration of the application of the AL-TENS (15 versus 30 minutes). Methods. A repeated-measures, intrasubject randomized experimental design was used, where each participant was his/her own control. 22 healthy volunteers underwent heat pain stimulations with a contact thermode before (pretest) and after (posttest) AL-TENS application (15 and 30 minutes). Outcome measures included subjective pain during AL-TENS, duration, and magnitude of AL-TENS-induced analgesia. Results. Survival analysis showed that the median duration of AL-TENS analgesia was 10 minutes following the application of either 15 or 30 minutes of AL-TENS. The magnitude of analgesia following either application was comparable at all points in time (P values > 0.05) and ranged between -20% and -36% pain reduction. Conclusion. Only half of the participants still had heat-pain analgesia induced by the AL-TENS at 15 minutes postapplication.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.165
Threshold uncertainty score0.685

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0000.000

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 head0.019
GPT teacher head0.299
Teacher spread0.280 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations5
Published2013
Admission routes2
Has abstractyes

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