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Record W2884543881 · doi:10.4172/2329-891x.1000260

White Paper: Pathways to Progress in Newborn Screening for Sickle Cell Disease in Sub-Saharan Africa

2018· article· en· W2884543881 on OpenAlexaff
Lewis L. Hsu, Obiageli Nnodu, Biobele J. Brown, Furahini Tluway, Shonda King, Livingstone Gayus Dogara, Crystal L. Patil, Sergey S. Shevkoplyas, Guillaume Lettre, Richard Cooper, Victor R. Gordeuk, Bamidele O. Tayo

Bibliographic record

VenueJournal of Tropical Diseases · 2018
Typearticle
Languageen
FieldMedicine
TopicHemoglobinopathies and Related Disorders
Canadian institutionsMontreal Heart Institute
FundersDoris Duke Charitable Foundation
KeywordsMalariaDiseaseMedicineNewborn screeningHealth careAnemiaSickle cell anemiaIntensive care medicinePediatricsImmunologyEconomic growthInternal medicine

Abstract

fetched live from OpenAlex

Sickle Cell Disease (SCD) is among the most common single-gene diseases in the world but evidence-based comprehensive health care has not been implemented where the highest prevalence of SCD occurs, in sub-Saharan Africa (SSA). It represents an urgent health burden, both in terms of mortality and morbidity with an estimated mortality of 8-16% in children under 5 years in SSA. Addressing the high mortality of SCD in SSA and for effective management of SCD, newborn screening (NBS) should be incorporated with prevention of infections (including pneumococcal septicaemia and malaria), parental education and support at all levels of healthcare provision to enable timely recognition. The NBS working group of the Africa Sickle Cell Research Network (AfroSickleNet) collaboration surveyed current projects in NBS in SSA, and current conditions that hinder more widespread implementation of NBS for SCD. Solutions based on new point-of-care testing technology to disseminate education, and implementation science approaches that leverage existing resources are proposed.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.044
Threshold uncertainty score0.502

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.016
GPT teacher head0.259
Teacher spread0.243 · 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 teacher head, 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

Citations58
Published2018
Admission routes1
Has abstractyes

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