Assessment of OncoQuébec, an oncology clinical trials search engine, on clinical trial recruitment in Quebec.
Bibliographic record
Abstract
e23110 Background: Q-CROC (Quebec-Clinical Research Organization in Cancer), a not-for-profit organization, has the mission to enhance both the knowledge of and accessibility to oncology clinical trials in Quebec. Q-CROC’s pioneering platform, OncoQuébec, connects participants with research teams, making clinical research more accessible to cancer patients and healthcare professionals. OncoQuébec, has a set of distinct features that include oncology-specific filters, a user-friendly interface, accurate information on recruiting clinical trials and a free support service available for both patients and healthcare professionals. Even though there are various search tools available worldwide, there are limited or no published data on their outcome data. Here, we describe the possible impact of OncoQuébec on patient recruitment in clinical trials. Methods: Studies from three major recruiting centers (with approximately 40% of oncology trials in Quebec), listed on OncoQuébec and having a ClinicalTrials.gov identifier were examined from January to December 2023. The number of users, number of views, and number of clicks to access research team’s contact information were extracted from OncoQuébec’s back office using Google Analytics 4. The number of patients recruited for each clinical study was collected through Q-CROC’s liaison coordinators in each center. Number of patients recruited was examined across studies that were categorized as “higher traffic” vs. “lower traffic” studies according to the median number of users, views and clicks. Rate ratios (RR) for studies with higher vs. lower traffic and 95% confidence intervals (CI) were estimated using binomial negative regression models. Results: Over the three centers, 312 studies were analyzed. These studies resulted in a cumulative total of 1,683 users, 2,747 views, and 429 clicks to study contact. A total of 662 patients were recruited. Studies with more traffic in terms of number of users, views or clicks had 1.5-2 times more patients recruited compared to those with less traffic. All centers showed consistent trends with similar rate ratios. Conclusions: The results suggest that OncoQuébec generates traffic to oncology clinical trials and could positively influence recruitment. Therefore, it is to the benefit of all sponsors and recruiting sites to have their clinical studies listed on OncoQuébec with the most up-to-date and accurate clinical trial information. Our plan is to make such a tool available to all Canadians. Categories Average number of patients recruited RR [95% CI] Users: Lower traffic studies [≤ 3 users] (n=163) Higher traffic studies [> 3 users] (n=149) 1.552.74 1.77 [1.13, 2.77] Views: Lower traffic studies [≤ 4 views] (n=146) Higher traffic studies [> 4 views] (n=166) 1.682.51 1.50 [0.95, 2.36] Clicks: Lower traffic studies [0 click] (n=146) Higher traffic studies [> 0 click] (n=166) 1.432.73 1.91 [1.22, 2.99]
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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 | 0.148 | 0.566 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.009 | 0.019 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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.
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".