Time for high-burden countries to lead the tuberculosis research agenda
Notice bibliographique
Résumé
At long last, tuberculosis (TB) is getting the political attention that it deserves, being the leading infectious killer of humans today.In 2016, there were 10.4 million estimated new TB cases, with over 1.7 million deaths [1].The G20 declaration of July 2017 included TB in the context of the need to respond to the antimicrobial resistance threat, following the group's meeting in Hamburg, Germany [2].In November 2017, for the first time, a WHO Global Ministerial Conference on TB was held in Moscow, Russia, culminating in the Moscow Declaration to End TB [3].This year, in September, the United Nations General Assembly (UNGA) will hold the first-ever high-level meeting on the fight against TB [4].While the political attention brings much needed hope, other developments provide cause for worry.The United States government, the largest funder of TB control and research, is rapidly scaling back on overseas aid, slashing billions from global health and humanitarian assistance [5].Canada, despite its progressive policies, is spending substantially less on international aid than comparable G7 countries [6].And while there are considerable uncertainties, Brexit could have a major impact on European Union international development and humanitarian policies and is expected to challenge the EU's role as the world's leading donor [7].The case for funding global health in general, and TB in particular, in this political climate will lack for attention as long as wealthy donor countries focus their priorities on populist and nationalist demands or short-term outcomes of a transactional nature.It is therefore critical for countries most affected by TB to step up, show leadership, and invest in TB control as well as research.Take the case of Brazil, Russia, India, China, and South Africa (BRICS), which together account for 46% of all incident cases of TB and 40% of all TB-related mortality [1].With strong, if uneven, economic growth in BRICS, and their growing stature and leadership in the political arena, these countries are well placed to lead the charge against a disease that is a leading killer of their citizens and a huge drain on their economies [8].In fact, investments in TB control can lead to a huge return on investments for these countries [9].Commendably, there are signs of the BRICS stepping up to deal with TB, commensurate with their disease burden and economic and scientific prowess [8].The BRICS Leaders Xiamen Declaration (2017) specifically mentioned the need to improve surveillance of TB and also agreed to set up a TB research network [10].In fact, the BRICS are now major producers of TB research [11].While the US remains the top producer of TB research in the past 2 decades, India and China have emerged as the second and third leading producers of TB research in recent years [11].Further, bibliometric analyses show that the average year-on-year increase in TB publications from the BRICS countries was, in the past decade, nearly double the overall year-on-year increase across all countries [11].
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 | 0,009 | 0,029 |
| Méta-épidémiologie (sens strict) | 0,003 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,001 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,003 | 0,003 |
| Communication savante | 0,009 | 0,009 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,013 | 0,024 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,039 | 0,025 |
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 ».