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Infrastructure, capabilities, and capacities required for clinical trials design and delivery: A rapid scoping review of recommendations and regulations

2025· preprint· en· W4408177092 on OpenAlexaff
Laura Merson, Karolina D. Witt, Arishay Hussaini, Ayesha Siddiqui, Eli Harriss, Steven Webb, Patricia Njuguna, Divya Shah, An‐Wen Chan, Robert Terry, Nandi Siegfried, Jeni Stolow, Emmanuelle Denis, Madiha Hashmi

Bibliographic record

VenueWellcome Open Research · 2025
Typepreprint
Languageen
FieldMedicine
TopicEthics in Clinical Research
Canadian institutionsUniversity of Toronto
FundersWellcome TrustDepartment for International Development, UK GovernmentBill and Melinda Gates Foundation
KeywordsGrey literaturePsycINFOClinical trialMEDLINESystematic reviewScopusMedicineQuality (philosophy)LegislationClinical study designBusinessPolitical sciencePathology

Abstract

fetched live from OpenAlex

Objective: Synthesise the published literature and national regulations on infrastructure, capabilities and capacities required to manage and quality assure clinical research. Introduction: The World Health Assembly (WHA) resolution 75.8 (2022) called "for a strengthened global architecture for coordinated and high-quality clinical trials". For this remit, infrastructure, capabilities, and capacities needed to design and deliver high-quality clinical trials must be understood and advanced. This rapid scoping review aims to identify the breadth of requirements and recommendations for effective management of clinical trials in regulations, national legislation and the published literature. The findings will be summarised into themes. It will inform a framework for the assessment and development of units undertaking observational studies and interventional clinical trials. Inclusion criteria: Peer-reviewed literature, grey literature, and national legislation that recommends infrastructure, capabilities, and/or capacities needed to manage and quality assure clinical trials. Publications authored by those who design, manage, fund, sponsor, regulate or oversee clinical trials. Methods: Peer-reviewed and grey literature will be identified through Medline, Embase, PsycINFO, and Global Health via Ovid; SCOPUS; the Web of Science Core Collection; and the WHO Global Index Medicus using specific field codes to increase the specificity of the search strings. No date, language, or geographic limits will be applied. Deduplicated titles and abstracts will be screened by two blinded reviewers with discrepancies resolved by a third reviewer. Grey literature may be identified through the peer reviewed literature, supplemented with structured searches of Google and DynaMed. National regulations will be sourced online and from available summaries. Full text literature and regulations will be screened by a single reviewer, with proportionate verification by a second reviewer. Data will be extracted and coded for patterns in NVivo software. All items and codes will be summarised using a thematic framework analysis and identify core constructs within each theme.

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)0.159
metaresearch head score (Gemma)0.225
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Science and technology studies, Research integrity
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.288
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.1590.225
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0010.000
Science and technology studies0.0000.003
Scholarly communication0.0000.000
Open science0.0010.005
Research integrity0.0010.004
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 head0.869
GPT teacher head0.689
Teacher spread0.180 · 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 designSystematic review
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

Citations1
Published2025
Admission routes1
Has abstractyes

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