Engine failures: A critical analysis of current clinical trial websites' search engines.
Bibliographic record
Abstract
e18537 Background: Clinical trials are critical to treatment advancement as they provide a foundation for future discoveries. Currently, only 7% of cancer patients in Ontario are enrolled in a clinical trial and 1 in 4 clinical trials fails to recruit enough patients. Enrolling patients depends on finding suitable trials for them. Lack of trial availability is the most common barrier to trial recruitment especially in non-academic hospitals since they tend to hold fewer within-center trials compared to larger centers. Most must rely on finding trials via databases such as ClinicalTrials.gov, yet there is a lack of literature that critically examines these databases for their searching capabilities emphasizing the need for critical analysis of the current search engines for delivery of a suitable list of clinical trials for patients. Methods: From June to September 2022, three individuals were trained and hired to conduct searches for 18 cancer patients across five search engines through the Clinical Trials Navigator program. Upon receiving referrals, they each searched online repositories to find eligible trials for the patient. Each navigator conducted an initial search for each patient on ClinicalTrials.gov. In addition, each searched alternative websites: Navigator 1 searched CanadianCancerTrials, Navigator 2 searched ClinicalTrialsOntario, and Navigator 3 searched Canadian Cancer Clinical Trials Network and OncoQuebec. For every search, each tracked search key words, total trials shown, total eligible trials found, and the number of eligible trials found on alternate websites that were not present in the initial ClinicalTrials.gov search. A qualitative analysis was done in tandem to identify shortcomings in the search engines. All searches were amalgamated by the lead navigator. Results: Our findings reveal pitfalls in the clinical trial search system, such as dysfunctional search filters, inconsistent results across the different websites, outdated trial information, and most importantly, lack of reproducibility as well as a lack of thoroughness of search results. On ClinicalTrials.gov, out of 54 search results, only 6 were reproducible between navigators. The only searches that were consistent between the navigators revealed zero trials for the patient. Not only was there variability in the total number of studies shown per same patient search by different navigators despite using similar or identical search terms, but there was also variability in the total number of eligible studies for the patient. Conclusions: The functionality of clinical trials search engines compromises equitable access to clinical trials, and impair clinical trials accrual. The highlighted challenges of the current clinical trials search engines available to patients and health care professions indicate an inefficient process that may be compromising clinical trial recruitment and thus potential patient outcomes.
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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 | 0.392 | 0.778 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.077 | 0.065 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.014 | 0.018 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".