A bibliometric analysis of immuno-oncology research: Is a new global leader emerging?
Notice bibliographique
Résumé
e13593 Background: The last decade has witnessed an increase in cancer research globally, and a transformation of the anti-cancer armamentarium. The biggest innovation has been development of immuno-oncology (IO) therapies, and specifically immune checkpoint inhibitors (ICIs), targeting CTLA-4 and PD-(L)1, reshaping treatment paradigms for many cancers (e.g., melanoma). Since the first ICI approval in 2011, research from countries in the European Union (EU) and North America has dominated this field. However, more recently research authored in China has emerged as a potential leader. In this study we analysed cancer and IO research outputs since 2011, exploring emerging national trends, and focusing on ICI development. Methods: IO research articles were identified using a high-resolution bibliometric method, previously validated. Relevant articles were identified from the Web of Science, utilising a complex search strategy of pre-defined keywords, including the 9 FDA-approved ICI and 8 ICI approved in China-only. Additional search filters were used to determine secondary characteristics including country (assigned a fractional count based on proportional authorship), collaborations, and tumour site. Results: Since 2011, over 175,000 cancer research articles have been published globally. The number of articles published annually has increased steadily, largely driven by increasing outputs from Chinese authors. China is now the leading global publisher of cancer research accounting for 18% of all outputs in 2021, and has consistently surpassed US and EU total outputs since 2019. Over the past decade there has been a steady increase in total IO research outputs, rising by 378% (n = 2926) compared to 2011. China began publishing significant IO research in 2014 and has risen rapidly to dominate this field. In 2021, China accounted for 37% of total IO outputs. The US was previously dominant, however there has been a steady decline in proportional outputs from 63% in 2012-12 to 29% in 2019-21. Only 17% of published Chinese IO research is internationally collaborative. Further, Chinese research focuses on tumour sites with a high national prevalence, such as oesophagus, stomach and liver, dominating research outputs in these areas. Research on the 9 FDA-approved ICI is dominated by the US, with only 7% having Chinese author contribution. Conversely, the 8 ICIs approved in China-only have negligible (< 5%) author contributions from the US and EU. Conclusions: The past decade has witnessed a substantial rise in both cancer and IO research. IO articles published by Chinese authors, frequently exclusively, now account for over a third of annual outputs. Those authored in China focus on cancers with a high national prevalence, and link to ICIs approved exclusively in China. With increasing global appetite for ICIs, especially from middle-income countries, ICIs developed in China may offer a viable alternative to current options.
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 | 0,030 | 0,162 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,004 | 0,004 |
| Bibliométrie | 0,191 | 0,282 |
| Études des sciences et des technologies | 0,002 | 0,002 |
| Communication savante | 0,010 | 0,008 |
| Science ouverte | 0,002 | 0,004 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,008 | 0,002 |
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 ».