Establishment of a baseline to measure academic clinical trial activity in Canada.
Bibliographic record
Abstract
e17547 Background: The Canadian Cancer Clinical Trials Network (3CTN) was created in 2013 in response to the State of Cancer Clinical Trials in Canada Report recommendation for pan-Canadian program to facilitate initiation and conduct of academic cancer clinical trials. Goals are 1) strengthen capability and capacity for multi-centre trials developed by the academic sector; 2) improve patient outcomes. Methods: To establish 3CTN: 1) Sites were surveyed about current trial activities, personnel and resources; 2) the 2011-2013 academic trials portfolio was created from cancer registries; 3) recruitment data was collected . Results: 47 Canadian sites responded to the survey. Atlantic Canada had the fewest numbers of trials, phase 1 trials, biospecimens and novel imaging capacity. Ontario conducted the most academic trials. Most sites did not have clinical trial management systems. Average time from protocol receipt ranged from 85 - 198 days. All sites had SOPs and training programs. The most commonly cited requirement to increase trial activity was funding. 67 sites in 9 provinces reported recruitment (Table). From 2011-2014, 172 new trials opened and 211 trials were closed; a loss of 39 trials to the portfolio. Conclusions: Canadian academic clinical trials activity is declining. Trial capabilities are diverse. 3CTN will direct activity and resources to increase capacity and efficiency to improve patient outcomes. Recruitment to 3CTN portfolio trials. Recruitment by Region Centres 2011 2012 2013 Mean Alberta 2 187 171 175 178 Atlantic Canada 8 145 116 79 113 British Columbia 6 177 183 127 162 Manitoba 1 111 90 95 99 Ontario 21 1548 1263 1232 1348 Quebec 12 618 654 823 704 Pediatrics 17 361 374 394 376 Total 67 3147 2851 2925 2974 Trials All Open Academic 616 620 624 559 single-center 366 352 373 336 multi-center 250 268 251 223 New trials 40 55 29 47 Closed trials 37 46 62 66 * Canadian Cancer Research Alliance (2011). Report on the State of Cancer Clinical Trials in Canada. Toronto: CCRA.
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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 | 0.024 | 0.053 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.011 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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; 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".