Patient-Oriented Biobanking for Pediatric Ocular Oncology: Development of a Patient Advocate Committee and Evaluation of Participant Consent Preferences in the Kids Eye Biobank
Bibliographic record
Abstract
Biobanks collect and store resources (e.g., biospecimens, images, and clinical data) for use in future research. Through centralizing data, biobanks establish robust data sets. This is valuable for advancing research in the field of pediatric ophthalmology, as many childhood eye and vision conditions, including cancer, are rare and challenging to investigate. Patient engagement in research can improve the relevance and impact of research being conducted. However, there is limited evidence on effective engagement methods in a pediatric biobank setting. This research sought to establish patient-oriented biobanking practices in the Kids Eye Biobank—a pediatric ophthalmology biobank with a dedicated rare pediatric eye cancer collection. The first aim of this was to develop a patient advocate committee in the Kids Eye Biobank. Further, the Kids Eye Biobank uses a broad informed consent model through which participants make decisions regarding how their resources can be shared and used. The second aim was to characterize the consent preferences of Kids Eye Biobank participants.
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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 | 0.366 | 0.351 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".