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Record W2297827672 · doi:10.1155/2016/9365326

Effect of Laser Acupuncture on Anthropometric Measurements and Appetite Sensations in Obese Subjects

2016· article· en· W2297827672 on OpenAlexaff
Chi‐Chuan Tseng, Alan Tseng, Jason Tseng, Chia‐Hao Chang

Bibliographic record

VenueEvidence-based Complementary and Alternative Medicine · 2016
Typearticle
Languageen
FieldMedicine
TopicAcupuncture Treatment Research Studies
Canadian institutionsUniversity of Toronto
FundersChiayi Chang Gung Memorial HospitalChang Gung Medical FoundationFondation pour la Recherche Médicale
KeywordsAnthropometryAcupunctureAppetiteMedicinePhysical therapyPsychologyInternal medicineAlternative medicine

Abstract

fetched live from OpenAlex

Purpose. A patient-assessor-blinded, randomized, sham-controlled crossover trial was performed to investigate the effectiveness of laser acupuncture on anthropometric measurements and appetite sensation in obese subjects. Methods. Fifty-two obese subjects were randomly assigned to either the laser acupuncture group or the sham laser acupuncture group. Subjects within each group received the relevant treatment three times a week for 8 weeks. After a two-week washout period, the subjects then received the treatment of the opposite group for another 8 weeks. BMI, body fat percentage, waist-to-hip ratio (WHR), waist circumference, hip circumference, and appetite sensations were measured before and after 8 weeks of treatment. Results. BMI, body fat percentage, WHR, waist circumference, and hip circumference decreased significantly (p < 0.05) in the laser acupuncture group compared to baseline but there was no decrease in those variables in the sham laser acupuncture group. Laser acupuncture significantly improved scores on the fullness, hunger, satiety, desire to eat, and overall well-being relative to the baseline (p < 0.05). Conclusions. Laser acupuncture is well tolerated and improves anthropometric measurements and appetite sensations in obese subjects.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.445
Threshold uncertainty score0.615

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
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 head0.111
GPT teacher head0.399
Teacher spread0.288 · 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 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

Citations22
Published2016
Admission routes1
Has abstractyes

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