Integrating the Sunnybrook Biobank into the Future of Healthcare - Exploring Patient and Healthworker Biobanking Perspectives
Bibliographic record
Abstract
Background The large scale collection of biological samples through biobanking generates an important resource for scientific research and personalized medicine. Excellent annotation of clinical samples is essential for researchers to assign clinical significance to their findings in both common and rare conditions. The practice of biobanking comes with ethical/medicolegal questions related to informed consent, control of samples, incidental findings, and withdrawal of consent. The success of biobanking is dependent on institutional support and the trust and goodwill of participants. Public awareness of the risks to personal information is increasing due to high profile data breaches. Present-day challenges with regards to digital privacy may influence potential participants' willingness to donate samples. . The Sunnybrook Hematology Biobank, “the Biobank”, has collected blood and bone marrow samples from patients with acute leukemia, myelodysplastic syndrome, myeloproliferative neoplasms and other hematologic disorders. The Biobank has collected over 800 samples from more than 600 patients since inception in 2021. To improve our practices, we conducted a survey of patients who had participated in biobanking (BB), patients who had not participated (NB), and health care workers/researchers (HCW) at Sunnybrook Health Sciences Centre in Toronto, Canada. Methods Survey questions were designed around themes of knowledge and support for biobanking, privacy and control, and trust. BB patients were given additional questions around their experiences. Similarly, HCW were given questions regarding integration of biobanking practices into their clinical practice. Responses were graded on a likert scale. The survey was conducted online through REDCap or through paper forms. The study was approved by the local Research Ethics Board and supported by an internal institutional grant. BB participants were contacted by telephone, NB participants were approached in clinic waiting rooms and online surveys for HCW were distributed through institutional email. Results 126 HCW, 101 NB, and 100 BB participants completed surveys. 59% of HCW, 35% of NB participants, and 75% of BB participants believed they had a good understanding of what biobanking is. The majority of respondents supported biobanking (96% HCW, 89% NB, 96% BB) and believed that more resources should be devoted to creating biobanks. Top concerns with regard to sample donation included concerns over genetic information used to re-identify participants for exclusionary purposes, for profit companies using their samples, and research being conducted that respondents may not agree with. NB participants had high levels of trust in Sunnybrook physicians (93%), hospital/government institutions and charitable foundations and less trust in insurance companies (9%) and for profit companies (13%) 77% of BB participants felt that the consent discussion provided them with sufficient understanding regarding biobank participation while 22% responded neutrally. 53% of NB and BB participants felt that they should be able to select which types of research their samples could be used for, and 82% of participants felt their healthcare experience would be made more meaningful by having donated. 52% of HCW were willing to adjust their workflow to support biobanking and 39% were neutral. In solicited open comments, the predominant theme amongst HCW was concern regarding potential non-use of biobanked samples. Discussion/Conclusion Overall, the majority of respondents supported biobanking and agreed that biobanking practices are important for research objectives. Respondents also felt a sense of meaning by participating in biobanking. Respondents indicated that they desired some control over the type of research that their samples were used for, and concern regarding privacy breaches was present to a lesser extent. Current BB participants reported high levels of comfort with the consent discussion, though a 22% neutral response leaves room for future improvement. An emerging theme from HCW participants was the desire for transparency and accountability as to the proper use of collected samples. These results will inform future practices to improve our consenting process, integrate sample collection and consent with clinical practices and provide transparency as to the research being conducted with biobanked samples.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.021 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.019 | 0.011 |
| Scholarly communication | 0.018 | 0.008 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".