Finding Canadian cancer clinical trials on the Internet: an exploratory evaluation of online resources.
Bibliographic record
Abstract
BACKGROUND: Online information about the availability of clinical trials promises to enhance the accrual of patients into trials. The primary objective of our study was to assess the completeness of online databases of breast cancer clinical trials available in Canada. METHODS: Eligible online resources were identified through a search of MEDLINE (articles published from 1966 to January 2002), an Internet search with Google, examination of Web sites of cancer organizations and information from experts. Resources were included if they contained information about open, active cancer clinical trials available in Canada. Web sites reviewed were not limited to those based in Canada. For each eligible resource, the number of listed trials and the proportion of trials identified were calculated. RESULTS: Of 30 Web sites identified, 8 met all of the inclusion criteria; 3 were based in Canada and 5 in the United States. The total number of breast cancer trials identified as being available in Canada was 28. The Physician Data Query (PDQ) Clinical Trials Database of the US National Cancer Institute (cancer.gov/search/clinical_trials) identified 86% (24/28). The database of the National Cancer Institute of Canada Clinical Trials Group (NCIC CTG) (ctg.queensu.ca) identified 29% (8/28) of the available breast cancer trials. CenterWatch Clinical Trials Listing Service (centerwatch.com) identified 4% (1/28). INTERPRETATION: If the PDQ database included all of the NCIC CTG trials, it would become the most complete database of breast cancer clinical trials currently available in Canada. Online cancer data sources should strive to make access to clinical trials simpler and more reliable, particularly for residents of the country where the trial is to be conducted.
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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 | 0.133 | 0.524 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.027 | 0.049 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".