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Record W6889176240 · doi:10.25504/fairsharing.41de90

FAIRsharing record for: WHO Guidelines for good clinical practice (GCP) for trials on pharmaceutical products

2022· dataset· en· W6889176240 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueFAIRsharing.org · 2022
Typedataset
Languageen
FieldEngineering
TopicAdvanced MIMO Systems Optimization
Canadian institutionsnot available
Fundersnot available
KeywordsGood clinical practiceClinical PracticeClinical trialGood manufacturing practiceGood practiceSet (abstract data type)Pharmaceutical industryGood laboratory practiceAlternative medicine

Abstract

fetched live from OpenAlex

This FAIRsharing record describes: The purpose of these Guidelines for Good Clinical Practice (GCP) for Trials on Pharmaceutical Products (WHO TRS 850 - Annex 3) is to set globally applicable standards for the conduct of such biomedical research on human subjects. They are based on provisions already promulgated in a number of countries, including Australia, Canada, European Community countries, Japan, Nordic countries (Denmark, Finland, Iceland, Norway and Sweden) and the United States. These provisions inevitably vary somewhat in content and emphasis, but all are consonant with regard to the prerequisites to be satisfied and the principles to be applied as a basis for assuring the ethical and scientific integrity of clinical trials. Indeed, they have provided a formal basis for mutual recognition of clinical data generated within the respective countries.

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.

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)0.008
metaresearch head score (Gemma)0.064
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.056
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.064
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.001
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 head0.348
GPT teacher head0.495
Teacher spread0.148 · 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