As bleak as it sounds? Analyzing trends in oncology clinical trial initiation in the United Kingdom from 2013 to 2022.
Bibliographic record
Abstract
93 Background: Clinical trials, especially phase 3 randomized clinical trials (RCTs), inform the standard of care in oncology, and their availability is the first step in ensuring opportunities for patient participation.A recent study by the Association of the British Pharmaceutical Industry showed a sharp decline in industry-sponsored clinical trials being performed in the United Kingdom (UK) over the past 5 years. In this study we sought to focus on UK participation in phase 3 RCTs of systemic anti-cancer therapy, both industry- and non-industry-sponsored, to evaluate if this decline applies more broadly to all phase 3 RCTs in the UK, especially within the context of Brexit in 2020. Methods: Data were identified and downloaded from ClinicalTrials.gov (CT.gov), a registry of privately and publicly funded clinical studies conducted globally, on March 14, 2023. Completed and recruiting trials with a start date on or after January 1, 2013, were identified using the keywords “neoplasms” and “cancer” and limited to “Phase 3”. Trials of supportive interventions or purely loco-regional interventions e.g., surgery, were excluded, as were randomized phase 2 trials. Eligible trials were categorized by year of commencement, participating countries, and the primary sponsor (e.g., industry or non-industry, including academic and cooperative groups). Results: 2345 of 3849 identified trials met the study eligibility criteria. The number of studies open in the UK increased from 48 in 2013 to 77 in 2021 but decreased to 49 in 2022. The number of UK trial sites included 383 in 2013 and decreased to 259 in 2022. Industry sponsored studies remained steady each year from 2017 to 2021 from 71 to 74 followed by a decline to 45 in 2022. In the European Union, excluding the UK, the number of open studies was 93 in 2013 and increased to 127 in 2021, but dropped to 93 in 2022. Combined trials in the US and Canada increased from 67 in 2013 to 105 in 2022. Conclusions: Clinical trials offer patients an avenue to experimental drugs and therapeutic combinations. Previous reports have raised concern that the UK has had precipitous drops in clinical trial initiation and enrollment secondary to the evolving regulatory landscape driven by concern about changes to the regulatory process and disharmonization due to Brexit. Our study highlights a reduction in clinical trials in the UK, however, this is consistent with declines in trial initiation across the EU, suggesting other factors (e.g., COVID-19) may be contributing factors. Paradoxically, trial initiation in the US and Canada increased over the same timeframe.[Table: see text]
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".