Comparison of investigator-initiated and industry-initiated trials in Canada and other countries.
Bibliographic record
Abstract
464 Background: Investigator-initiated trials (IITs) developed by academic investigators without inherent commercial interests have contributed significantly to oncology. However, costs and regulatory complexity may negatively impact IITs. Little is published about current trends in cancer IITs. We focused on IITs in genitourinary cancers (GU IITs) in Canada and other countries and compared these to industry-initiated trials conducted over the same time period. Methods: We collected data using searches of the ClinicalTrials.gov registry for bladder, kidney, and prostate cancer trials conducted in Canada, US, and France from 2007 to 2017. IITs were defined as trials funded by individuals, universities, or organizations. We then characterized trials based on type, purpose, phase, participants, masking, assignment, and allocation. Results: Of the 498 GU trials conducted in Canada from 2007 to 2017, 201 (40%) were IITs, and 254 (51%) were industry-initiated. On average, GU IITs in Canada increased by 88% from 2007 to 2017, while industry-initiated trials increased by 30%. Although the increase of GU IITs in Canada was greater than industry-initiated trials, the growth was larger in US and France. In US, 746 (29%) of the 2537 GU cancer trials were IITs and increased by an average of 106% from 2007 to 2017, while industry-initiated trials increased by 61%. In France, 164 (41%) of the 405 GU cancer trials were IITs and increased by an average of 125% from 2007 to 2017, while industry-initiated trials increased by 83%. For GU IITs in Canada, most studies were interventional (85%) with purpose of treatment (47%). Phase 2 trials (23%) and trials with 20 to 49 participants (29%) accounted for the majority. Most trials were randomized (41%), open-label (70%), and used single group assignment (43%). GU IITs in US and France shared these same patterns in their characteristics. Conclusions: The increase in GU IITs from 2007 to 2017 was lower in Canada than US and France. In all three countries, more GU trials were industry-initiated than IITs. More emphasis must be placed on IITs in Canada through increased commitment and funding by institutions to promote development of IITs and maintain their distinctive, academically driven intentions.
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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 | 0.102 | 0.270 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.008 | 0.017 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".