Globalization of oncology clinical trials: Which lower-middle and upper-middle income countries are participating?
Notice bibliographique
Résumé
e13512 Background: With globalization of cancer research, many randomized controlled trials (RCTs) led by high income countries (HICs) are now enrolling patients from lower- and upper-middle income countries (LMICs/UMICs). While enrolling diverse global populations promotes research collaborations, there are unanswered questions about which countries participate in RCTs and how this may contribute to global research capacity. Here we describe which UMICs/LMICs participate in RCTs led by HICs. Methods: The study cohort was identified from a database of all oncology RCTs (systemic/surgery/RT) published globally during 2014-2017. The study cohort was restricted to RCTs led by HICs which enrolled participants from LMIC/UMICs. We used a bibliometric approach to explore whether the participation of UMICs/LMICs in RCTs led by HICs was as expected based on other measures of cancer research activity. Country-level bibliometric output for 2007-2017 was identified in the Web of Science database. We compared RCT participation (i.e. % of RCTs in our cohort that each LMIC/UMIC participated in) with country-level cancer research bibliometric output (i.e. % of total cancer research bibliometric output from the same group of countries that came from a specific LMIC/UMIC). Results: The global cohort included 694 RCTs; 636 (92%) of which were led by HICs. Among the HIC-led trials, 187 (29%) enrolled patients in LMICs (n=84) and/or UMICs (n=182); this formed the study cohort. The most common participating LMICs were India (50% of trials, 42/84), Ukraine (46%, 39/84), Philippines (25%, 21/84), and Egypt (14%, 12/84). The most common participating UMICs were Russian Federation (63% of trials, 115/182), Brazil (50%, 91/182), Romania (34%, 61/182), China (31%, 56/182), Mexico (31%,56/182) and South Africa (30%, 54/182). Several LMICs are over-represented in our cohort of RCTs based on proportional cancer research bibliometric output: Ukraine (46% of RCTs but 2% of cancer research bibliometric output), Philippines (25% RCTs, 1% output), Georgia (8% RCTs, 0.2% output). Several UMICs are also over-represented in the study cohort of RCTs including Russia (63% RCTs, 2% output), Romania (34% RCTs, 2% output), Mexico (31% RCTs, 2% output) and South Africa (30% RCTs, 1% output). The inverse relationship was seen for China (31% RCTs, 69% output). Conclusions: A substantial proportion of RCTs led by HICs enroll patients in LMICs/UMICs. The LMICs/UMICs which participate in these trials are not as one would expected based on overall cancer bibliometric output as a surrogate for research ecosystem maturity. Reasons for this apparent discordance and how these data may inform future capacity strengthening activities require further study.
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 | 0,209 | 0,415 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,002 |
| Bibliométrie | 0,012 | 0,031 |
| Études des sciences et des technologies | 0,002 | 0,004 |
| Communication savante | 0,009 | 0,006 |
| Science ouverte | 0,002 | 0,007 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,007 | 0,001 |
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; l’étiquette directe de Gemma et le classifieur distillé Codex s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».