Contributions to Global Hematology from Low and Middle-Income Countries: Insights from ASH 2018
Notice bibliographique
Résumé
Background. Establishing research capacity in low and middle-income countries (LMIC) is key for improving health systems and implementing actionable programs through evidence-based assessments. Few studies have analyzed hematology research capacity in LMICs. The American Society of Hematology (ASH) annual meeting is the largest hematology event where peer-reviewed contributions from researchers worldwide are selected for presentation based on scientific merit. Therefore, it can provide a useful snapshot of the current status of hematology research in a single point in time. For this reason, we analyzed abstracts presented at the 2018 ASH annual meeting (ASH18) with a focus on those from authors working in a LMIC. Objective. To describe the proportion of abstracts presented at ASH18 from an LMIC and analyze their characteristics as a surrogate for academic contributions to global hematology. Methods. We reviewed all abstracts presented at ASH18 in an oral presentation or poster form published online in the supplemental edition of Blood 2018;132 (Suppl 1). LMICs were selected according to the World Bank classification including countries or territories with a gross national income <$12,056 USD per capita. We described all abstracts that had a co-author with an affiliation from an institution in a LMIC, regardless of their position. We categorized studies as clinical vs. basic and single vs. multicenter nature. We also identified the presence of conflict-of interest statements (COI) and identifiable industry sponsors. We compared abstracts that had co-authors from high-income countries (HIC+LMIC) vs. those from LMIC countries alone. Comparison across groups was performed using chi-square and Fisher's exact test. Results. A total of 4,871 abstracts presented at ASH 2018, with 1,026 oral presentations and 3,845 posters were available online. Among them, 510 abstracts (10.5%) had a contributing author from an institution in an LMIC, corresponding to 92 (9%) of all oral presentations and 418 (10.9%) of all posters (Figure 1). LMIC-only contributions represented 4.7% of all abstracts (n=229). The most common LMIC of origin for LMIC-only contributions was China with 133 (58.1%) (Figure 1). Most abstracts were clinical and multicentric in nature (62 and 70.2%, respectively), and in 42.5% of them a COI was reported. Clinical trials reflected 19% of all LMIC contributions. In 31.9% of cases the first author was affiliated to an institution in a HIC. Mixed LMIC/HIC contributions had significantly more COIs and industry sponsors than those from LMIC-only institutions (Table 1). When comparing between oral vs poster LMIC presentations, works selected for an oral presentation were significantly more clinical and multicentric, had a higher proportion of clinical trials, more COIs and identified industry sponsors (Table 2). Conclusions. LMICs, where more than 80% of the world population resides, were responsible for only a small fraction of contributions to ASH18, half of them representing a form of international collaboration, with a high number of COI disclosures. Disclosures Gomez-Almaguer: Amgen: Consultancy, Speakers Bureau; Janssen: Consultancy, Speakers Bureau; Teva: Consultancy, Speakers Bureau; Takeda: Consultancy, Speakers Bureau; Celgene: Consultancy, Speakers Bureau.
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 | 0,029 | 0,090 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,016 | 0,021 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,008 | 0,005 |
| Science ouverte | 0,001 | 0,005 |
| Intégrité de la recherche | 0,002 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,020 | 0,003 |
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 ».