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Record W2611871817 · doi:10.1177/2156587217706617

The Effects of the Bali Yoga Program for Breast Cancer Patients on Chemotherapy-Induced Nausea and Vomiting: Results of a Partially Randomized and Blinded Controlled Trial

2017· article· en· W2611871817 on OpenAlexaff
Annélie S. Anestin, Gilles Dupuis, Dominique Lanctôt, Madan Bali

Bibliographic record

VenueJournal of Evidence-Based Complementary & Alternative Medicine · 2017
Typearticle
Languageen
FieldMedicine
TopicNausea and vomiting management
Canadian institutionsUniversité du Québec à Montréal
Fundersnot available
KeywordsNauseaMedicineVomitingChemotherapyBreast cancerRandomized controlled trialCancerInternal medicinePhysical therapyOncology

Abstract

fetched live from OpenAlex

Complementary and alternative medicine has been shown to be beneficial in reducing chemotherapy-induced nausea and vomiting. However, conclusive results are lacking in order to confirm its usefulness. The purpose of this study was to determine whether a standardized yoga intervention could reduce these adverse symptoms. This was a partially randomized and blinded controlled trial comparing a standardized yoga intervention with standard care. Eligible patients were adults diagnosed with stages I to III breast cancer receiving chemotherapy. Patients randomized to the experimental group participated in an 8-week yoga program. There was no significant difference between the experimental and control groups on chemotherapy-induced nausea and vomiting after 8 weeks. Results suggest the yoga program is not beneficial in managing these adverse symptoms. However, considering preliminary evidence suggesting yoga's beneficial impact in cancer symptom management, methodological limitations should be explored and additional studies should be conducted.

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)0.004
metaresearch head score (Gemma)0.009
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 score0.004
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.009
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.003
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.107
GPT teacher head0.409
Teacher spread0.301 · 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

Citations26
Published2017
Admission routes1
Has abstractyes

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