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Record W7117367749 · doi:10.1002/1545-5017.70066

Returning Aggregate Results to Research Participants/Families: Updated Recommendations From the Children's Oncology Group

2025· article· en· W7117367749 on OpenAlexaff
Kathleen S. Ruccione, Stacey Crane, Tina Bocking, Joan Darling, Conrad V. Fernandez, Beth Fisher, Gina Martin, Wendy Pelletier, Rebecca D. Pentz, Heidi Pusztay, Blair Segers, Chris Williams‐Hughes, Kimberly A. Pyke‐Grimm

Bibliographic record

VenuePediatric Blood & Cancer · 2025
Typearticle
Languageen
FieldMedicine
TopicChildhood Cancer Survivors' Quality of Life
Canadian institutionsDalhousie UniversityUniversity of CalgaryEion (Canada)
Fundersnot available
KeywordsMultidisciplinary approachQuality (philosophy)Aggregate (composite)Outcomes researchClinical OncologyAggregate data

Abstract

fetched live from OpenAlex

In 2012, the Children's Oncology Group (COG) issued 18 recommendations for returning aggregate research results to participants. This manuscript reviews the Return of Results (ROR) Committee's progress in implementing these recommendations. A multidisciplinary review assessed implementation status, identified challenges, and incorporated relevant literature to propose updates and future research directions. Fourteen recommendations were fully or partially implemented, three were deemed infeasible, and one is planned for future action. Successful implementation was supported by infrastructure, education, and ongoing quality improvement. This experience offers a practical model for other cooperative research groups aiming to return research results beyond clinical trials.

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)0.537
metaresearch head score (Gemma)0.585
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.463
Threshold uncertainty score0.571

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.5370.585
Meta-epidemiology (narrow)0.0020.003
Meta-epidemiology (broad)0.0040.011
Bibliometrics0.0090.006
Science and technology studies0.0040.005
Scholarly communication0.0110.012
Open science0.0120.016
Research integrity0.0170.021
Insufficient payload (model declined to judge)0.0060.005

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 head0.071
GPT teacher head0.417
Teacher spread0.346 · 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 designNot applicable
DomainMethods
GenreMethods

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
Published2025
Admission routes1
Has abstractyes

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