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Record W2985818748 · doi:10.1182/blood-2019-130053

Contributions to Global Hematology from Low and Middle-Income Countries: Insights from ASH 2018

2019· article· en· W2985818748 on OpenAlexaff
Andrés Gómez‐De León, Perla R. Colunga Pedraza, Luz del Carmen Tarín Arzaga, Emmanuel Bugarín-Estrada, David Gómez-De León, Lillian Sung, David Gómez‐Almaguer

Bibliographic record

VenueBlood · 2019
Typearticle
Languageen
FieldMedicine
TopicGlobal Health and Surgery
Canadian institutionsSickKids FoundationHospital for Sick Children
Fundersnot available
KeywordsGross national incomeHematologyMedicineDeveloping countryFamily medicinePolitical scienceInternal medicineEconomic growth

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.029
metaresearch head score (Gemma)0.090
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.029
Threshold uncertainty score0.153

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0290.090
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0160.021
Science and technology studies0.0010.001
Scholarly communication0.0080.005
Open science0.0010.005
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0200.003

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.

Opus teacher head0.008
GPT teacher head0.267
Teacher spread0.259 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2019
Admission routes1
Has abstractyes

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