Examination of current status of cross-provincial border access for clinical trials for patients with cancer.
Bibliographic record
Abstract
e18661 Background: Clinical trials are the gold standard for establishing new and reliable treatment options. Even though 80% of cancer patients state that they would be interested in entering a clinical trial, only 3% of adults with cancer participate in clinical trials due to several challenges. Through a Clinical Trials Navigator program, we have curated clinical trials for approximately 243 patients and 7% have entered onto clinical trials. However, none of the patients were referred to trials outside of their home province due to a lack of clarity on the cross-border referral process. The goal of this study is to analyze the availability of patients for participating in out-of-province clinical trials in the presence of a lack of policy for provincial border referrals. Methods: This study was conducted by doing clinical trial searches for cancer patients using several clinical trial search engines such as ClinicalTrials.Gov, CanadianCancerTrials.com, ClinicalTrialsOntario, 3CTN, and Q-CROC. We also used a search engine to compare the number of clinical studies available in various cities for the four most common types of cancer in Canada. After this, an analysis was performed on over 243 clinical trial searches. Results: Out of the 18 patients we observed, most of them had potential out-of-province trials, but none of them joined out-of-province trials because of the lack of information regarding the process. Also, province disparity in trial availability was found for the most common cancers in Canada. The numbers of trials available in Ontario and Quebec are much higher than the number of trials available in PEI and Newfoundland which is almost zero or zero. The home treatment center of most of the patients involved in this study was Windsor Regional Hospital, and all of them were from Ontario. Among the patients that were referred, most of them were referred to Princess Margaret Cancer Centre in Toronto. Again, none of the patients were referred to cancer centers out of Ontario. Conclusions: In conclusion, our findings reveal the province disparity in the number of clinical trials available. The lack of policies that inform Canadians on how to cross provincial boundaries to enter a clinical trial prevents them from receiving the most needed cutting-edge treatments, compromising potential patient outcomes. Therefore, we need more clarity on policies for the cross-border referral process.
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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 | 0.084 | 0.310 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.024 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".