Contributions to the American Society of Hematology Meeting From Low- and Middle-Income Countries: An In-Depth Analysis and Call to Action
Bibliographic record
Abstract
PURPOSE: Establishing research capacity in low- and middle-income countries (LMICs) is key for improving the outcomes of patients with hematologic diseases globally. Few studies have analyzed the contributions of LMICs to global hematology. The American Society of Hematology Meeting (ASH) is the largest international academic event where peer-reviewed contributions in our field are presented. METHODS: In this cross-sectional analysis, all abstracts accepted to ASH 2018 selected for a poster or oral presentation were reviewed. Those that had a contributing author from an LMIC were identified. The proportion of LMIC abstracts across categories was analyzed. Country of origin, high-income country participation, the presence of a conflict of interest (COI), and sponsorship were determined. RESULTS: From 4,871 abstracts reviewed, 506 had a contributing author from an LMIC (10.4%), with 277 (54.7%) contributions in partnership with a high-income country. LMIC-independent contributions corresponded to 19 of 1,026 oral abstracts (1.9%) and 209 of 3,845 posters (5.4%). Most abstracts from LMICs were clinical (n = 311; 61.5%) and multicentric in nature (n = 353; 69.8%). COI statements with the pharmaceutical industry were common (n = 214; 42.3%). Collaboration between LMICs was infrequent (n = 33; 6.5%). Upper-middle-income countries had 466 participations (81.5%), in comparison with 96 (16.8%) in low-middle-income and 10 (1.7%) in low-income countries. CONCLUSION: LMICs were responsible for a small fraction of abstracts at ASH18; low-income countries were practically absent. Almost half of accepted works represented a form of international collaboration, with clinical, multicenter studies predominating and COI disclosures a frequent and unexpected feature, reflecting the instrumental nature of LMIC participation and a lack of independent, robust, locally developed hematology research.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.031 | 0.089 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".