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Record W3154573190 · doi:10.1016/s2214-109x(20)30539-8

Urgently seeking efficiency and sustainability of clinical trials in global health

2021· review· en· W3154573190 on OpenAlexaff
Jay Park, Rebecca F. Grais, Monica Taljaard, Etheldreda Nakimuli‐Mpungu, Fyezah Jehan, Jean B. Nachega, Nathan Ford, Denis Xavier, André Pascal Kengne, Per Ashorn, M. Eugenia Socías, Zulfiqar A Bhutta, Edward J. Mills

Bibliographic record

VenueThe Lancet Global Health · 2021
Typereview
Languageen
FieldEconomics, Econometrics and Finance
TopicHealth Systems, Economic Evaluations, Quality of Life
Canadian institutionsMcMaster UniversityImpactCentre for Global Health ResearchBritish Columbia Centre on Substance UseUniversity of British ColumbiaHospital for Sick ChildrenUniversity of OttawaOttawa Hospital
FundersFogarty International CenterMQ: Transforming Mental HealthMedical Research CouncilBill and Melinda Gates Foundation
KeywordsSustainabilityGlobal healthClinical trialEnvironmental healthMedicinePublic healthBiologyNursingInternal medicine

Abstract

fetched live from OpenAlex

This paper shows the scale of global health research and the context in which we frame the subsequent papers in the Series. In this Series paper, we provide a historical perspective on clinical trial research by revisiting the 1948 streptomycin trial for pulmonary tuberculosis, which was the first documented randomised clinical trial in the English language, and we discuss its close connection with global health. We describe the current state of clinical trial research globally by providing an overview of clinical trials that have been registered in the WHO International Clinical Trial Registry since 2010. We discuss challenges with current trial planning and designs that are often used in clinical trial research undertaken in low-income and middle-income countries, as an overview of the global health trials landscape. Finally, we discuss the importance of collaborative work in global health research towards generating sustainable and culturally appropriate research environments.

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 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)0.307
metaresearch head score (Gemma)0.044
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Meta-epidemiology (broad)
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.934
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.3070.044
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0190.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.711
GPT teacher head0.644
Teacher spread0.067 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreReview

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

Citations63
Published2021
Admission routes1
Has abstractyes

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