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Assessment of Onco+, a personalized navigation support, for oncology clinical trials in Quebec.

2025· article· en· W4410811592 on OpenAlexaffabout
C. Vayssier, Lucie D'Amours, Marco Décelles, Alain Berube, Mariam Mehran, Jean-Paul Bahary

Bibliographic record

VenueJournal of Clinical Oncology · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetics, Bioinformatics, and Biomedical Research
Canadian institutionsCentre Hospitalier de l’Université de MontréalQuebec Breast Cancer FoundationQuebec - Clinical Research Organization in Cancer
FundersAbbViePfizer
KeywordsMedicineClinical trialOncologyClinical OncologyPersonalized medicineInternal medicineMedical physicsFamily medicineCancerBioinformatics

Abstract

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e23114 Background: Quebec-Clinical Research Organization in Cancer (Q-CROC), a not-for-profit organization, has the mission to improve accessibility to oncology clinical trials in Quebec. Onco+, Q-CROC’s navigation support in collaboration with Quebec Cancer Foundation (QCF), addresses the growing need for a personalized patient support in accessing clinical trials. Onco+ is a free service available on OncoQuébec, Q-CROC’s web-application for oncology clinical trial search. Here, we describe the use of Onco+ since its launch in 2022. Methods: The assessment period spanned from May 2022 to December 2024. When users submitted a request for a trial search, they first provided consent to share their personal information, then completed a form with 29 questions to help refine the search. User support at QCF was provided by an experienced nurse trained in oncology trials by Q-CROC with complementary services like accommodation and psychological support offered as needed. The requests made from healthcare professionals (HCP) were treated by Q-CROC. The search for trials was done using OncoQuébec. All requests were addressed within 2-3 business days. A survey was sent 4-6 weeks after receiving the service from QCF. Results: A total of 269 requests were treated during the assessment period (90% from patients/caregivers; 10% from HCP). Most requests (n = 171; 64%) were matched with study proposals. Reasons for no match were: no clinical trial identified (n = 28; 10%) and other factors (e.g. incomplete information, medical unfitness, pending tests, etc.; n = 70; 26%). A total of 586 studies were proposed, with some suggested multiple times. Most studies were industry sponsored (n = 470; 80%). The survey results revealed that 84% of users were satisfied with the service, 88% found it very useful and 90% would recommend it (n = 48). Additionally, 28% had contacted a research team and 5% were involved in a clinical trial (n = 40). Onco+ referencing included OncoQuébec/Q-CROC, QCF, the treating physician, and patient associations. The table below summarizes patient data from the matched requests. Conclusions: During the assessment period, Onco+ was accessed by an average of 9 users per month, mostly through organic search. Almost 2/3 of requests were matched. Most requests were for advanced cancers, with over half from outside Quebec's major centers.The success of this service highlights the need to help patients access clinical trials. We plan to make this service widely available to all Canadians. Age (years; n=130) 19-40 : 18 (14%)41-60 : 56 (43%)61+ : 56 (43%) Gender (n=170) Male: 74 (44%)Female: 96 (56%) Predominant cancer type ≥10% of requests (n=170) Breast: 37 (22%)Colorectal: 23 (14%)Lung: 18 (11%)Pancreatic: 17 (10%) Stage of cancer (n=151) Stage 1: 9 (6%)Stage 2: 17 (11%)Stage 3: 18 (12%)Stage 4: 107 (71%) Region (n=160) Greater Montreal: 48 (30%)Québec city: 26 (16%)Other regions: 86 (54%)

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)0.017
metaresearch head score (Gemma)0.060
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.983
Threshold uncertainty score0.594

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0170.060
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0020.001
Scholarly communication0.0030.002
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0280.004

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 head0.210
GPT teacher head0.595
Teacher spread0.385 · 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.

Study designObservational
DomainMethods
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".

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Citations0
Published2025
Admission routes2
Has abstractyes

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