Cross sectional analysis of clinical trials search results for cancer patients using a navigator-assisted clinical trials search using five different search engines
Bibliographic record
Abstract
BACKGROUND: Clinical trials play a critical role in providing patients with access to novel treatments and therapies. However, limitations in clinical trial search engines impede healthcare professionals and patients from accessing the most suitable clinical trials. This study aimed to address this issue by conducting a critical analysis of several prominent clinical trial search websites, including ClinicalTrials.gov, Canadian Cancer Trials, Clinical Trials Ontario, Canadian Cancer Clinical Trials Network, and Q-CROC. METHODS: To identify areas for improvement, three skilled clinical trials navigators independently curated clinical trial searches for 18 cancer patients over a 2-month period. After verifying patients' eligibility for enrollment in clinical trials, the navigators documented their search outcomes and identified several limitations in the current search engines. RESULTS: Careful curation of clinical trials for 18 patients revealed 247 trials. However, 140 eligible trials out of 247 (57% with 95% binomial confidence interval [50%, 63%]) were found only on alternative websites yet not discoverable on the initial ClinicalTrials.gov searches, even though they were listed on ClinicalTrials.gov. Our study revealed multiple deficiencies in available clinical trials search engines. Lack of reliability was repeatedly identified in all search engines. DISCUSSION: This study highlights that the current clinical trial search system needs improvement to enhance patient outcomes. It needs to be highlighted that these searches were performed by trained and dedicated clinical trials navigators. The challenges facing patients and health care professionals in navigating would be much greater. The findings from this study can serve as a foundation for the development of enhanced search engines with improved functionality, which will enable healthcare professionals and patients to find and access the most suitable clinical trials with greater ease and accuracy.
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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 | 0.043 | 0.194 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.016 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".