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Record W4224296361 · doi:10.12927/hcq.2022.26772

The Retinoblastoma Research Booklet: A Catalyst for Patient Involvement in Retinoblastoma Research

2022· article· en· W4224296361 on OpenAlexaffvenue
Ivana Ristevski, Jay Kiew, Mitch Hendry, Michelle Prunier, Roxanne Noronha, Mawj Al-Hammadi, Kaitlyn Flegg, Brenda L. Gallie, Katherine Paton, Helen Dimaras

Bibliographic record

VenueHealthcare Quarterly · 2022
Typearticle
Languageen
FieldMedicine
TopicOcular Oncology and Treatments
Canadian institutionsInstitute for Clinical Evaluative SciencesBC Research (Canada)Vancouver General HospitalOntario Neurotrauma FoundationCanadian Patient Safety InstituteSickKids FoundationRegional Municipality of WaterlooUniversity of British Columbia Hospital
Fundersnot available
KeywordsRetinoblastomaHealth professionalsMedicineMedical educationNursingFamily medicineHealth carePolitical science

Abstract

fetched live from OpenAlex

Peer-to-peer recruitment efforts are important in generating interest and participation of patients as partners in research but difficult to sustain when face-to-face interactions are limited. The Retinoblastoma Research and You! booklet, co-developed by patients, researchers and health professionals, serves as a guide for patient engagement in research while retaining an element of personalization. The Retinoblastoma Research and You! booklet was developed through two virtual workshops to iterate and finalize the booklet design and content. The booklet outlines how individual patients' lived experiences and skills can influence retinoblastoma research and highlights real-world examples of patient-partnered research activities at different stages of the research process.

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)0.015
metaresearch head score (Gemma)0.048
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.063
Threshold uncertainty score0.210

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.048
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0040.002
Scholarly communication0.0060.005
Open science0.0020.009
Research integrity0.0050.009
Insufficient payload (model declined to judge)0.0630.037

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 head0.084
GPT teacher head0.417
Teacher spread0.333 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations5
Published2022
Admission routes2
Has abstractyes

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