Clinical Trial Availability: An Analysis of Limited Clinical Trial Options for Cancer Patients in Ontario
Bibliographic record
Abstract
BACKGROUND: Clinical research has developed successful treatments for patients, creating a critical source of hope for those who have exhausted standard of care treatment, allowing access to innovative therapies which may positively impact quality of life or survival outcomes. Despite this, less than 5% of patients are enrolled in a clinical trial.PROBLEMA barrier to patient enrolment in clinical trials is the strict eligibility criteria for participation. When searching for a clinical trial, the number of results returned to the patient is often an inaccurate representation of the number of trials that they are eligible for, revealing a key misconception that there are plenty of clinical trials available for patients.METHODSThis study uses Clinical Trials Navigators (CTNs) to conduct searches for patients across multiple clinical trial search engines. During their search, CTNs recorded the number of trials returned by each search and assessed the eligibility criteria of each result to determine the number of trials that the patient is truly eligible for. Physicians then determined which trials are appropriate given the patient's health history.RESULTSOur findings reveal out of 241 patients, there were only 1-3 trials identified as non-phase 1; or non-phase 1/2 with the median being 1 eligible trial per patient. There is limited data on how many trials these patients successfully enrolled in after physician review, but it is anticipated to be a low percentage.CONCLUSIONOverall, this study emphasizes the limited availability of clinical trials for patients, contributing to low enrolment statistics in Ontario.
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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 | 0.019 | 0.150 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.018 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.000 |
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".