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P-102 The Regulatory and Legal Complexity of the Multicentre, Multinational Clinical Trial with TP05

2016· article· en· W2327856611 on OpenAlexaff
Robert Hofmann, Christine Mueller-Rosenau, Swantje Petersen

Bibliographic record

VenueInflammatory Bowel Diseases · 2016
Typearticle
Languageen
FieldMedicine
TopicBiomedical Ethics and Regulation
Canadian institutionsRobarts Clinical Trials
Fundersnot available
KeywordsMultinational corporationClinical trialBureaucracyPharmacyBusinessDescriptive statisticsMedicineAccountingFinanceFamily medicinePolitical scienceLaw

Abstract

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The evidence provided by multinational clinical trials is of outstanding importance for new drugs, new medical devices and new procedures. Large multinational randomised clinical trials are under a financial and organizational threat because of the burden of complex regulations imposed by governments and authorities. The burden is affecting administration, costs and timing. The “overbureaucracy” is justified by protection of data integrity and by protection of the safety of patients. In most of the cases, it is only possible for the industry to manage these trials with the help of CROs (contract research organization). Some authors speak of “mountains of red tape” or “stifled innovation” and “mountains of paperwork” when addressing clinical trial bureaucracy. A quantitative review the paperwork of a major multinational, multicenter clinical trial has to our knowledge not yet been performed. All agreements, contracts and permits necessary for the clinical trial “TP05 for the Treatment of Mild to Moderate Active UC (Precision-UC)” with the Clinicaltrials.gov Identifier: NCT01903252 were analyzed. The number of countries originally approached was 34, covering all continents, with 22 countries in Europe and North America finally participating. The planned number of patients is 800. There are around 180 investigators participating. We looked at investigators, CROs, free-lancers, hospitals, pharmacies, patients, governments, regulatory agencies, IRBs, transport and logistics companies, manufacturer and others. If identified as a legal or regulatory requirement, any legal document or permit became a counting unit. The statistics are descriptive only. Until end of March 2015 we identified 1789 contracts and agreements between the CRO and the sites (64 were in countries which in the end did not participate), 58 contracts between the CRO and subcontractors, 32 contracts of the sponsor (main one with the CRO), 104 permits issued by authorities and institutions, and 3 agreements with couriers and depots. A multinational, multicenter clinical trial of this size requires around 2000 contracts, agreements and permits with a ratio of 1:10 per investigator and 1:2.5 per patient. This amount can only be handled with dedicated legal and administrative support. This also explains why the industry cannot manage such trials without specialized CROs and it also explains why these kinds of trials almost never can be handled purely by the academic community. To minimize the bureaucratic burden, contracts should be rigorously standardized and IRBs as wells as competent authorities should reciprocally acknowledge votes. Absent such steps, there is not much opportunity for improvement to the status quo.

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)0.129
metaresearch head score (Gemma)0.356
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.871
Threshold uncertainty score0.681

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1290.356
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0020.006
Science and technology studies0.0010.005
Scholarly communication0.0080.006
Open science0.0020.002
Research integrity0.0030.006
Insufficient payload (model declined to judge)0.0500.006

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 head0.043
GPT teacher head0.320
Teacher spread0.278 · 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 designQualitative
DomainMethods
GenreEmpirical

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

Citations0
Published2016
Admission routes1
Has abstractyes

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