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CONSORT Extension for Chinese Herbal Medicine Formulas 2017: Recommendations, Explanation, and Elaboration (Simplified Chinese Version)

2017· article· en· W4245595112 on OpenAlexaff
Chung Wah Cheng, Taixiang Wu, Hongcai Shang, Youping Li, Douglas G. Altman, David Moher, Zhaoxiang Bian

Bibliographic record

VenueAnnals of Internal Medicine · 2017
Typearticle
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicPharmacological Effects of Natural Compounds
Canadian institutionsOttawa HospitalUniversity of Ottawa
FundersCancer Research UK
KeywordsElaborationMedicineExtension (predicate logic)Traditional medicineTraditional Chinese medicineConsolidated Standards of Reporting TrialsAlternative medicineHumanitiesComputer sciencePhilosophy

Abstract

fetched live from OpenAlex

Editors' Note: This article is the simplified Chinese version of the CONSORT Extension for Chinese Herbal Medicine Formulas 2017: Recommendations, Explanation, and Elaboration. (Cheng C, Wu T, Shang H, Li, Y, Altman D, Moher D; CONSORT-CHM Formulas 2017 Group. CONSORT Extension for Chinese Herbal Medicine Formulas 2017: Recommendations, Explanation, and Elaboration. Ann Intern Med. 2017;167:112-21. [Epub 27 June 2017]. doi:10.7326/M16-2977).

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)0.118
metaresearch head score (Gemma)0.521
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Reporting · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.882
Threshold uncertainty score0.625

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1180.521
Meta-epidemiology (narrow)0.0040.004
Meta-epidemiology (broad)0.0100.012
Bibliometrics0.0130.014
Science and technology studies0.0020.004
Scholarly communication0.0080.006
Open science0.0040.006
Research integrity0.0100.014
Insufficient payload (model declined to judge)0.1870.061

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 head0.198
GPT teacher head0.535
Teacher spread0.336 · 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 designNot applicable
DomainReporting
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

Citations27
Published2017
Admission routes1
Has abstractyes

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