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Record W4225563524 · doi:10.2478/jhp-2022-0007

Development of decision-making considerations to support equitable patient selection in paediatric haemophilia trials

2022· article· en· W4225563524 on OpenAlexafffundabout
Anne Fu, Karen Strike, Korinne Hamilton, Linda Waterhouse, Kay Decker, Theresa M. Almonte, Anthony K.C. Chan

Bibliographic record

VenueThe Journal of Haemophilia Practice · 2022
Typearticle
Languageen
FieldMedicine
TopicHemophilia Treatment and Research
Canadian institutionsMcMaster UniversityMcMaster Children's HospitalRegional Municipality of NiagaraHamilton Health Sciences
FundersHamilton Health Sciences FoundationMcMaster UniversityHamilton Health Sciences
KeywordsHaemophiliaMedicineClinical trialInclusion (mineral)Selection (genetic algorithm)Set (abstract data type)Haemophilia BEconomic JusticeMedical educationFamily medicineHaemophilia APsychologyPediatricsSocial psychologyComputer sciencePolitical science

Abstract

fetched live from OpenAlex

Abstract Background Clinical trials for investigational haemophilia treatments such as gene therapy offer a potentially life-changing opportunity to those who are selected for enrolment. However, the number of enrolment slots available for these trials is often greatly exceeded by the number of eligible patients. Many of the strategies that are commonly used to select candidates for participation can be highly unsystematic, inequitable, and subjective. A more rigorous set of criteria is therefore needed to evaluate each candidate's suitability for trial participation in order to eliminate bias in selection and fulfill the ethical principle of justice. Aims To review current knowledge and issues in patient selection for paediatric haemophilia clinical trials with competitive availability, and to develop a more objective standard for decision-making that takes into account the needs of all involved parties. Methods A literature search on the ethics of trial participant selection and the practice of fairly distributing limited medical resources was conducted to identify previous literature and best practices in the area. A list of essential decision-making considerations was then designed to guide the selection of paediatric participants for haemophilia therapy trials through iterative group discussions between a diverse team of health professionals at McMaster Children's Hospital, Hamilton, ON, Canada. Results Current practices in resolving this ethical issue are highly heterogenous, although there are some common themes and recommendations. The three main criteria supported by the team and the literature search for inclusion in the considerations were: medical need, need for support, and potential safety considerations for the patient. Three measures for evaluating each criterion were developed and added for consideration during the decision-making process. The role of patient selection in meeting the scientific aims of the trial was also considered. Conclusion Attempting to create an equitable, systematic decision-making procedure for clinical trial participant selection involves a wide variety of competing values and ethical considerations, and discrepancies between recommendations are commonplace. The criteria presented here are intended to be used as a guideline to assist the equitable selection of paediatric patients for participation in haemophilia clinical trials with highly limited enrolment, although it may have some applicability to other areas of clinical research or therapeutic areas concerned with the allocation of scarce medical resources. Next steps should involve speaking with patients, community members and other stakeholders in order to include their perspectives. Assessment of medical need, potential support needs, and safety considerations form the basis of criteria for discussions around how to make enrolment in paediatric haemophilia clinical trials more equitable © Shutterstock

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)0.015
metaresearch head score (Gemma)0.015
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.585
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0150.015
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.090
GPT teacher head0.405
Teacher spread0.314 · 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 teacher head, not a consensus.

Study designOther design
Domainnot available
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
Published2022
Admission routes3
Has abstractyes

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