Exploring Demographic Representation and Reporting in Lung Cancer Clinical Trials with Canadian Sites from 2013 to 2023
Bibliographic record
Abstract
This review evaluates the reporting of demographic characteristics and the diversity of participants of phase III lung cancer clinical trials with Canadian research sites. A literature search was conducted using the ClinicalTrials.gov registry to identify clinical trials conducted between 1 January 2013, and 31 December 2023. The demographic reporting practices and the representation of sex/gender, racial, and ethnic groups were assessed. The location of Canadian research sites was also examined for trends in reporting and representation. Associated publications were reviewed for demographic data collection methods. Of the 25 clinical trials, 24 reported race and 18 also reported ethnicity. All clinical trials reported sex/gender, and the city and province of the participating Canadian sites. Most participants were White (66.1%), identified as not Hispanic or Latino (81.4%), and were male (57.8%). The provinces with the most clinical trial sites were Ontario (43.6%) and Quebec (34.2%). Lung cancer clinical trials lack adequate demographic reporting and representation of females, diverse patient groups, and geographical locations in Canada with high lung cancer incidence rates. Specifically, the Indigenous Peoples of Canada and Nunavut require better representation in lung cancer clinical trials conducted in Canada. These findings highlight the need to improve diversity and demographic representation in clinical research.
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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 | 0.053 | 0.212 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.019 | 0.038 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".