Clinical trials navigator: Patient-centered access to clinical trials.
Bibliographic record
Abstract
e14024 Background: Despite recommendations from premier institutions such as NCCN that all cancer patients should be entered on clinical trials, only 3 – 5 % of adult cancer patients are enrolled on clinical trials in North America. The reason for this is multi-factorial and includes poor trial design, inappropriate endpoints, inappropriate inclusion/exclusion factors, attitudes about trial participation held by patient and/or treating physician and lack of trial availability. Methods: To address issues of trial availability, in March 2019, we initiated a novel service to help Canadian patients find clinical trial options. The service compares patient demographic and health status information provided against potential opportunities sourced using clinicaltrials.gov and Canadian clinical trials websites. A report presenting outcomes of CTN review is developed for the requesting patient or physician. An interview is provided for the self-referring patient by supporting physicians. Results: To date 96 patients have used this service. Most (94%) were stage IV or refractory/ relapsed. Smaller disease sites represented 23% of our patient population (brain, sarcoma, pancreas). Our turn-around-time from request of services to delivery of report to patient or physician improved over time and is currently 24 hours during the working week. Of those eligible, 25% of patients died before referral, the median time from referral to the CTN to the patient’s death was 109 days (3 – 188 days). Conclusions: Significant interest from both physicians and patients for this service was identified. Strategies are being developed to encourage earlier referrals to clinical trials would improve number of patients entering clinical trials as 25% of our patients. [Table: see text]
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 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 | 0.023 | 0.109 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.439 | 0.184 |
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".