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Record W2790420707 · doi:10.4414/smw.2017.14556

Insufficient recruitment and premature discontinuation of clinical trials in Switzerland: qualitative study with trialists and other stakeholders

2017· article· en· W2790420707 on OpenAlexaff
Matthias Briel, Bernice S. Elger, Erik von Elm, Priya Satalkar

Bibliographic record

VenueSwiss Medical Weekly · 2017
Typearticle
Languageen
FieldMedicine
TopicEthics in Clinical Research
Canadian institutionsMcMaster UniversityImpact
Fundersnot available
KeywordsDiscontinuationMedicineClinical trialContext (archaeology)Qualitative researchFamily medicineResearch ethicsCompromiseNursingPathologySurgery

Abstract

fetched live from OpenAlex

AIMS OF THE STUDY: Premature discontinuation occurs in about 25% of randomised clinical trials in Switzerland; it mainly affects investigator-initiated trials and is mostly due to problems with recruitment of patients. The aim of this study was to qualitatively investigate reasons for trial discontinuation due to poor patient recruitment and suggestions to address those reasons in the Swiss context. METHODS: We conducted semi-structured interviews with trialists whose trials were discontinued because of recruitment problems, other experienced trialists, and stakeholders in clinical research in Switzerland. Interviews were audio-recorded, transcribed verbatim, and anonymised. We analysed the transcripts using deductive coding and built up themes that were continuously discussed within the research team. RESULTS: Of 65 invited Swiss trialists and stakeholders, 39 (60%) agreed to be interviewed and contributed to this analysis. We identified four main themes of reasons for poor recruitment: (1) Switzerland has a decentralised healthcare system with many small hospitals and few patients per hospital, many research regulations, no standardisation of medical records across hospitals, and a heterogeneous ethics assessment of study protocols. There is little collaboration of different stakeholders in clinical research and a lack of prioritisation of projects. (2) Limited human and financial resources, especially in the academic setting, compromise research questions and size of clinical trials. When funding is used up this typically triggers discontinuation of already delayed clinical trials. (3) Investigators face underdeveloped research networks and a limited collaborative attitude among clinical researchers. They typically embark on clinical studies with a great deal of optimism but insufficient preparation. (4) Swiss patients have universal health coverage and many treatment options. Negative media coverage of clinical research and a lack of accessible information for patients about ongoing clinical studies frequently make participation in clinical trials less attractive. More interactive structures and collaboration across stakeholders were mentioned as potential solutions to tackle the problems. CONCLUSIONS: Recruitment of participants into clinical trials in Switzerland is challenging because of various, often interlinked factors related to the Swiss health system, available funding, investigators, and patients. Common goals and concerted efforts by involved stakeholders appear necessary to achieve improvement.

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)0.046
metaresearch head score (Gemma)0.066
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.954
Threshold uncertainty score0.244

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0460.066
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0100.011
Scholarly communication0.0040.004
Open science0.0020.006
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0030.000

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 head0.884
GPT teacher head0.712
Teacher spread0.172 · 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

Citations26
Published2017
Admission routes1
Has abstractyes

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