Abstract 287: Trial Haven: the Ethical Risks of Offshore Mechanical Thrombectomy Trials
Bibliographic record
Abstract
Introduction Randomized controlled trials (RCTs) in low‐ and middle‐income countries (LMICs) offer significant potential for advancing global health. However, these RCTs often face conflicts between pharmaceutical industry incentives and global health priorities. The high costs, strict regulations, and extensive requirements of Phase 3 trials make LMICs more appealing than high income countries (HICs). For MT trials, LMICs offer advantages such as higher stroke rates, lower costs, more flexible regulations, shorter timelines, and a strong willingness to participate from both patients and local clinicians. However, the industry's motivations extend beyond these practical considerations. These collaborative trials present several ethical concerns, including power imbalances between industry or HICs investigators and those from LMICs, vulnerabilities of patients and clinicians in LMICs, health system unprepared for RCTs and the limited availability of interventions to locals once the RCTs are concluded. Major ethical issues include using control arms (SMT) that fall below the standard of care in HICs and inadequate post‐protocol treatment. Accepting inferior SMT and minimal post‐protocol care in LMICs hospital settings can introduce bias favoring experimental devices. Data from these RCTs, used for FDA approval, could lead to negative outcomes for patients in both LMICs and HICs. We plan to do a global mapping of current RCTs scenario in MT for acute ischemic stroke. Methodology: The search terms “Stroke,” “Large Vessel Occlusion,” and “Mechanical Thrombectomy” were entered into the ClinicalTrials.gov search form. Filters were then set to show only new RCTs that are either currently recruiting participants or are planning to recruit. RCTs that were completed, terminated, or had unknown statuses were excluded. Results Our search identified 44 ongoing RCTs across various countries. The USA leads with 14 trials (25.45%), followed by China with 13 trials (23.64%), and France with 8 trials (14.55%). Turkey and Brazil each have 3 trials (5.45%), while Spain has 2 trials (3.64%). Argentina, Canada, Germany, Hungary, India, Israel, Italy, the Netherlands, Pakistan, Paraguay, Poland, and Taiwan each have 1 trial (1.82%). In terms of funding sources, industry funded 18 trials (40.91%), the NIH funded 1 trial (2.27%), and other sources funded 25 trials (56.82%). Of the industry‐sponsored trials, 10 (55.55%) are conducted offshore, 5 (27.77%) are in the USA, and 3 (16.66%) involve both domestic and international sites. Conclusion Globalizing MT RCTs offers benefits but also risks if oversight is inadequate. It's crucial to ensure that trials benefit local populations and local needs. Advocacy should prioritize locally relevant, need based, investigator‐initiated stroke trials in LMICs, rather than industry‐driven “parasitic” or “parachute” RCTs that may exploit LMICs patients for drug approvals in HICs. Even non‐industry‐funded trials in LMICs can be influenced by industry interests through collaborating researchers from HICs who is getting industry benefits. Rigorous ethical oversight is essential to prevent exploitation and ensure the integrity of these trials.
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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 | 0.341 | 0.571 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.002 | 0.013 |
| Scholarly communication | 0.013 | 0.012 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.012 | 0.011 |
| Insufficient payload (model declined to judge) | 0.039 | 0.007 |
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".