Abstract 287: Trial Haven: the Ethical Risks of Offshore Mechanical Thrombectomy Trials
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,341 | 0,571 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,003 | 0,003 |
| Bibliométrie | 0,005 | 0,009 |
| Études des sciences et des technologies | 0,002 | 0,013 |
| Communication savante | 0,013 | 0,012 |
| Science ouverte | 0,004 | 0,007 |
| Intégrité de la recherche | 0,012 | 0,011 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,039 | 0,007 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».