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Record W4221137585 · doi:10.1136/bmj-2021-067476

How to design high quality acupuncture trials—a consensus informed by evidence

2022· article· en· W4221137585 on OpenAlexafffund
Yuqing Zhang, Ruimin Jiao, Claudia M. Witt, Lixing Lao, Jianping Liu, Lehana Thabane, Karen J. Sherman, Mike Cummings, Dawn P. Richards, Eun-Kyung Anna Kim, Tae‐Hun Kim, Myeong Soo Lee, Michael E. Wechsler, Benno Brinkhaus, Jun J. Mao, Caroline Smith, Wei-Juan Gang, Baoyan Liu, Zhishun Liu, Yan Liu, Hui Zheng, Jiani Wu, Alonso Carrasco‐Labra, Mohit Bhandari, P.J. Devereaux, Xiang‐Hong Jing, Gordon Guyatt

Bibliographic record

VenueBMJ · 2022
Typearticle
Languageen
FieldMedicine
TopicAcupuncture Treatment Research Studies
Canadian institutionsRobarts Clinical TrialsMcMaster UniversityImpact
FundersNational Center for Complementary and Integrative HealthAbbott DiagnosticsNational Cancer InstituteRegeneron PharmaceuticalsNational Institutes of HealthGuangzhou UniversityGuangzhou University of Chinese MedicineUniversität ZürichChina Academy of Chinese Medical SciencesGenentechNational Natural Science Foundation of ChinaTechnische Universität MünchenIncyteEli Lilly CanadaCerecorSanofiAstraZenecaEli Lilly and CompanyMemorial Sloan-Kettering Cancer CenterGlaxoSmithKlineMcMaster UniversityAmgenTeva Pharmaceutical IndustriesU.S. Department of Veterans Affairs
KeywordsAcupunctureAlternative medicineClinical trialMedicineMedical physicsRandomized controlled trialRelevance (law)Quality (philosophy)Evidence-based medicinePhysical therapyResearch designMEDLINEFamily medicineIntensive care medicineSurgeryPathologyPolitical scienceSocial science

Abstract

fetched live from OpenAlex

An international panel including patients, clinicians, researchers, acupuncture and surgery trialists, statisticians, and experts in clinical epidemiology and methodology have developed new guidance for randomised controlled trials in acupuncture. It addresses the most prevalent and critical concerns of current acupuncture trials and will help funding agencies, trial registers, and journal editors to evaluate the relevance, importance, and quality of submitted trial proposals and completed trials

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)0.640
metaresearch head score (Gemma)0.717
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Methods · Consensus signal: Methods
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.360
Threshold uncertainty score0.444

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.6400.717
Meta-epidemiology (narrow)0.0050.010
Meta-epidemiology (broad)0.0200.018
Bibliometrics0.0170.010
Science and technology studies0.0080.015
Scholarly communication0.0300.024
Open science0.0160.016
Research integrity0.0350.041
Insufficient payload (model declined to judge)0.0070.010

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 head0.285
GPT teacher head0.477
Teacher spread0.191 · 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designTheoretical or conceptual
DomainMethods
GenreMethods

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

Citations71
Published2022
Admission routes2
Has abstractyes

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