Engine failures: A critical analysis of current clinical trial (CT) websites' search engines
Bibliographic record
Abstract
Background: Clinical trials are critical to treatment advancement as they provide a foundation for future progression. The existing clinical trial search system is composed of websites through which one can find recruiting clinical trials that a patient may be eligible for. Currently, only 7% of cancer patients in Ontario are enrolled in a clinical trial, emphasizing the need for optimization and critical analysis of the current search system for delivery of a suitable list of clinical trials for patients.MethodsThree individuals were hired to conduct searches for cancer patients across five search engines. They each conducted searches on ClinicalTrials.Gov. In addition, navigator 1 searched CanadianCancerTrials.com, navigator 2 searched ClinicalTrialsOntario, and navigator 3 searched 3CTN and Q-CROC. 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. Also, qualitative analysis was done to identify shortcomings in the search engines. All searches were amalgamated by the lead navigator.ResultsOur findings reveal pitfalls in the clinical trial search system, such as inadequate or dysfunctional search filters, inconsistent results across the different clinical trial websites, low reproducibility of search results, outdated trial information, and lack of user-friendly navigation. Final results will be available at the time of the conference.ConclusionThe highlighted challenges of the current search system indicate an inefficient process that may be compromising clinical trial recruitment and thus potential patient outcomes.
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 | 0.541 | 0.840 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.065 | 0.050 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.017 | 0.020 |
| Open science | 0.007 | 0.009 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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; 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".