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Record W2558083009 · doi:10.1111/bjh.14437

The evolutionary and clinical implications of the uneven distribution of the frequency of the inherited haemoglobin variants over short geographical distances

2016· article· en· W2558083009 on OpenAlexaff
Anuja Premawardhena, Angela Allen, Frédéric B. Piel, Chris Fisher, Laxman Perera, Rexan Rodrigo, Gayan Goonathilaka, Lebbe Ramees, Tim Peto, Nancy F. Olivieri, D. J. Weatherall

Bibliographic record

VenueBritish Journal of Haematology · 2016
Typearticle
Languageen
FieldMedicine
TopicHemoglobinopathies and Related Disorders
Canadian institutionsUniversity of Toronto
FundersNational Institute for Health and Care ResearchWellcome TrustMedical Research CouncilWellcomeUniversity of OxfordWorld Health Organization
KeywordsEthnic groupMalariaConsanguinityDistribution (mathematics)Variation (astronomy)GeographyAltitude (triangle)DemographyHeterozygote advantageEvolutionary biologyBiologyGeneticsAlleleImmunologyGene

Abstract

fetched live from OpenAlex

Studies of the frequency of heterozygous carriers for common inherited diseases of haemoglobin in over 7500 adolescent children in 25 districts in Sri Lanka have disclosed a highly significant variation over very short geographical distances. A further analysis of these findings, including their relationship to the past frequency and distribution of malaria, climatic variation, altitude, ethnic origin and consanguinity rates, have provided evidence regarding the evolutionary basis for the variable distribution of these conditions over short distances. It is likely that the complex interplay between malaria and the environment, together with related ethnic and social issues, exists in many countries across the tropical belt. Hence, these observations emphasise the importance of micromapping heterozygote distributions in high-frequency countries in order to define their true burden and the facilities required for the prevention and management of the homozygous and compound heterozygous disorders that result from their interaction.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.021
Threshold uncertainty score0.793

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
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 head0.011
GPT teacher head0.273
Teacher spread0.263 · 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

Citations34
Published2016
Admission routes1
Has abstractyes

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