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Record W4214586381 · doi:10.1136/bmj-2022-070533

Increasing the usefulness of acupuncture guideline recommendations

2022· article· en· W4214586381 on OpenAlexaff
Yuqing Zhang, Liming Lu, Nenggui Xu, Xiaorong Tang, Xiaoshuang Shi, Alonso Carrasco‐Labra, Holger J. Schünemann, Yaolong Chen, Jun Xia, Guang Chen, Jianping Liu, Baoyan Liu, Jiyao Wang, Amir Qaseem, Xiang‐Hong Jing, Gordon Guyatt, Hong Zhao

Bibliographic record

VenueBMJ · 2022
Typearticle
Languageen
FieldMedicine
TopicComplementary and Alternative Medicine Studies
Canadian institutionsMcMaster UniversityImpact
FundersGuangzhou UniversityNational Center for Complementary and Integrative HealthGuangzhou University of Chinese MedicineChina Academy of Chinese Medical SciencesNational Natural Science Foundation of China
KeywordsGuidelineAcupunctureMedicineMedical physicsAlternative medicinePathology

Abstract

fetched live from OpenAlex

Yu-Qing Zhang and colleagues examine the progress and pitfalls in guideline recommendations for acupuncture and provide suggestions for improvement

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)0.421
metaresearch head score (Gemma)0.832
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.421
Threshold uncertainty score0.714

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.4210.832
Meta-epidemiology (narrow)0.0010.002
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0150.011
Science and technology studies0.0030.005
Scholarly communication0.0150.022
Open science0.0070.013
Research integrity0.0100.013
Insufficient payload (model declined to judge)0.0080.002

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 head0.099
GPT teacher head0.390
Teacher spread0.291 · 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.

Study designTheoretical or conceptual
Domainnot available
GenreCommentary

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

Citations55
Published2022
Admission routes1
Has abstractyes

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