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Record W3097794040 · doi:10.1177/2054358120964119

Ethical Issues in the Design and Conduct of Pragmatic Cluster Randomized Trials in Hemodialysis Care: An Interview Study With Key Stakeholders

2020· article· en· W3097794040 on OpenAlexafffund
Stuart G. Nicholls, Kelly Carroll, Charles Weijer, Cory E. Goldstein, Jamie Brehaut, Manish M. Sood, Ahmed A. Al‐Jaishi, Erika Basile, Jeremy Grimshaw, Amit X. Garg, Monica Taljaard

Bibliographic record

VenueCanadian Journal of Kidney Health and Disease · 2020
Typearticle
Languageen
FieldMedicine
TopicEthics in Clinical Research
Canadian institutionsLondon Health Sciences CentreMcMaster UniversityUniversity of OttawaImpactInstitute for Clinical Evaluative SciencesWestern UniversityOttawa Hospital
FundersCanadian Institutes of Health ResearchCanada Research Chairs
KeywordsMedicineHemodialysisRandomized controlled trialKey (lock)NursingFamily medicineInternal medicineComputer securityComputer science

Abstract

fetched live from OpenAlex

BACKGROUND: Pragmatic cluster randomized trials (CRTs) offer an opportunity to improve health care by answering important questions about the comparative effectiveness of treatments using a trial design that can be embedded in routine care. There is a lack of empirical research that addresses ethical issues generated by pragmatic CRTs in hemodialysis. OBJECTIVE: To identify stakeholder perceptions of ethical issues in pragmatic CRTs conducted in hemodialysis. DESIGN: Qualitative study using semi-structured interviews. SETTING: In-person or telephone interviews with an international group of stakeholders. PARTICIPANTS: Stakeholders (clinical investigators, methodologists, ethicists and research ethics committee members, and other knowledge users) who had been involved in the design or conduct of a pragmatic individual patient or cluster randomized trial in hemodialysis, or their role would require them to review and evaluate pragmatic CRTs in hemodialysis. METHODS: Interviews were conducted in-person or over the telephone and were audio-recorded with consent. Recorded interviews were transcribed verbatim prior to analysis. Transcripts and field notes were analyzed using a thematic analysis approach. RESULTS: Sixteen interviews were conducted with 19 individuals. Interviewees were largely drawn from North America (84%) and were predominantly clinical investigators (42%). Six themes were identified in which pragmatic CRTs in hemodialysis raise ethical issues: (1) patients treated with hemodialysis as a vulnerable population, (2) appropriate approaches to informed consent, (3) research burdens, (4) roles and responsibilities of gatekeepers, (5) inequities in access to research, and (6) advocacy for patient-centered research and outcomes. LIMITATIONS: Participants were largely from North America and did not include research staff, who may have differing perspectives. CONCLUSIONS: The six themes reflect concerns relating to individual rights, but also the need to consider population-level issues. To date, concerns regarding inequity of access to research and the need for patient-centered research have received less coverage than other, well-known, issues such as consent. Pragmatic CRTs offer a potential approach to address equity concerns and we suggest future ethical analyses and guidance for pragmatic CRTs in hemodialysis embed equity considerations within them. We further note the potential for the co-creation of health data infrastructure with patients which would aid care but also facilitate patient-centered research. These present results will inform planned future guidance in relation to the ethical design and conduct of pragmatic CRTs in hemodialysis. TRIAL REGISTRATION: Registration is not applicable as this is a qualitative study.

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)0.273
metaresearch head score (Gemma)0.340
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.727
Threshold uncertainty score0.897

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.2730.340
Meta-epidemiology (narrow)0.0010.002
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0160.017
Scholarly communication0.0090.011
Open science0.0030.011
Research integrity0.0070.012
Insufficient payload (model declined to judge)0.0030.001

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 head0.608
GPT teacher head0.546
Teacher spread0.062 · 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

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

Citations10
Published2020
Admission routes2
Has abstractyes

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