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Record W4405048438 · doi:10.1182/blood-2024-211086

Patient Reflections on the Consent Process for Donating Extra Bone Marrow Research Samples to a Hematology Biobank: A Qualitative Interview Study

2024· article· en· W4405048438 on OpenAlexaffabout
Madeleine Gordon, Oksana Motalo, Erika Camilleri, Taryn Chesser, G. Davis, Grace Fox, Katya Godard, Caryn Y. Ito, Jocelyn Lepage, Krystina B. Lewis, Wendy Nuttall, Craig Peloshok, Stuart G. Nicholls, Mitchell Sabloff

Bibliographic record

VenueBlood · 2024
Typearticle
Languageen
FieldMedicine
TopicEthics in Clinical Research
Canadian institutionsOttawa HospitalUniversity of Ottawa
Fundersnot available
KeywordsMedicineBone marrowBiobankInformed consentClinical researchHematologyQualitative researchIntensive care medicineInternal medicineFamily medicineSurgeryPathologyAlternative medicineBioinformatics

Abstract

fetched live from OpenAlex

Background: Bone marrow samples taken at the time of diagnosis are essential for early diagnosis and treatment of acute leukemia (AL) and additionally can be used to understand the underlying biology. This requires bone marrow samples to be taken for clinical purposes as well as for research. Due to the rapid onset and progression of AL, there is a narrow time frame from initial disease suspicion to diagnosis confirmation and treatment initiation. This can present challenges with consenting patients for additional, research-specific bone marrow samples to be obtained during the diagnostic procedure. While patients with cancer are generally positive towards contributing to medical research, recruitment rates for bone marrow samples are low, hindering translational research aimed at improving patients' outcomes in AL. Little is known about patient perspectives with respect to the informed consent process under these time-sensitive circumstances, or how these processes can improve research sample provision while also maintaining respect for patient autonomy and supporting their decision-making during a stressful time. The current study is aimed to better understand the experiences of patients with AL in relation to consenting to provide extra bone marrow samples for research during the diagnostic procedure. Methods: Semi-structured interviews were conducted with patients treated for AL between January 1, 2017 and December 31, 2021 at The Ottawa Hospital. Patients were eligible if they were admitted to hospital urgently to confirm AL diagnosis, received intensive induction chemotherapy and spoke English or French. Interviews proceeded until data saturation was reached. Results: Seventeen patients were interviewed. Patient experiences centred on three key areas within the consent process: Preparation and awareness of research, logistical challenges related to obtaining consent within the limited time frame and having emotional and psychological support. Patients were supportive of increasing public knowledge about research and noted the important roles that friends and family members played in providing support and retaining information. Despite the time pressure and anxiety that came with a diagnosis of AL, the decision to give a research sample did not in itself require much deliberation. Decisions were informed by proximal factors such as impact on patient health and family, the anticipated pain associated with the bone marrow procedure and its duration, as well as distal factors such as altruism and trust in the healthcare team. Patients valued as much time as possible between the consent process and the bone marrow extraction. Further, they valued information about the level of anticipated procedure-associated pain, the purpose of ongoing research and its use of samples, and details regarding the privacy and security of the research samples. Conclusion: Our findings suggest that the success of consenting for additional bone marrow samples for research may be optimised through multiple changes, such as those pertaining to the environment where the consent discussion takes place as well as a period of time for reflection on the discussion prior to the procedure, in addition to the type of information provided, the recognition of patient concerns surrounding discomfort and how it will be mitigated and, finally, the value of current and future research.

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)0.050
metaresearch head score (Gemma)0.074
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Research integrity
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.996
Threshold uncertainty score0.264

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0500.074
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0130.015
Scholarly communication0.0070.007
Open science0.0030.009
Research integrity0.0040.009
Insufficient payload (model declined to judge)0.0040.001

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 head0.876
GPT teacher head0.712
Teacher spread0.163 · 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
Published2024
Admission routes2
Has abstractyes

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