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Record W2776268627 · doi:10.1002/pbc.26901

Pediatric oncology clinical trial participation where the geography is vast: Development of a clinical research system for tertiary and satellite centers in Ontario, Canada

2017· article· en· W2776268627 on OpenAlexaffabout
Sarah Alexander, Mark Greenberg, David Malkin, Carol Portwine, Donna L. Johnston, Mariana Silva, Shayna Zelcer, Samantha Sonshine, Janet Manzo, Carla Bennett, Kathy Brodeur‐Robb, Catherine Deveault, N. Ramachandran, Paul Gibson

Bibliographic record

VenuePediatric Blood & Cancer · 2017
Typearticle
Languageen
FieldMedicine
TopicChildhood Cancer Survivors' Quality of Life
Canadian institutionsOntario Institute for Cancer ResearchKingston General HospitalChildren's Hospital of Eastern OntarioLondon Health Sciences CentreMcMaster Children's HospitalC17 CouncilUniversity of TorontoPediatric Oncology GroupSickKids FoundationHospital for Sick Children
Fundersnot available
KeywordsMedicineCenter of excellenceTertiary careExcellenceClinical trialPediatric oncologyFamily medicineClinical OncologyPediatric cancerWork (physics)CancerInternal medicinePolitical science

Abstract

fetched live from OpenAlex

Opportunities for participation in clinical trials are a core component of the care of children with cancer. In Ontario, many pediatric patients live long distances from their cancer center. This paper describes the work that was done in order to allow patients participating in Children's Oncology Group trials to receive care, including research protocol related care, jointly between the tertiary pediatric cancer center and the closer-to-home satellite center. The system is a pragmatic risk-based model, supporting excellence in care while ensuring good conduct of the research in compliance with applicable regulations and guidelines, including ethics oversight.

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)0.078
metaresearch head score (Gemma)0.043
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.888
Threshold uncertainty score0.812

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0780.043
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.004
Science and technology studies0.0130.006
Scholarly communication0.0060.002
Open science0.0050.008
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0060.001

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 head0.192
GPT teacher head0.479
Teacher spread0.286 · 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
Published2017
Admission routes2
Has abstractyes

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