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Record W1998339219 · doi:10.1182/blood-2010-06-289488

Adaptation to anemia in hemoglobin E-β thalassemia

2010· article· en· W1998339219 on OpenAlexaff
Angela Allen, Christopher A. Fisher, Anuja Premawardhena, Tim Peto, Stephen Allen, Mahinda Arambepola, V Thayalsutha, Nancy F. Olivieri, D. J. Weatherall

Bibliographic record

VenueBlood · 2010
Typearticle
Languageen
FieldMedicine
TopicHemoglobinopathies and Related Disorders
Canadian institutionsUniversity Health Network
FundersNational Institute for Health and Care ResearchWellcome TrustMarch of Dimes Foundation
KeywordsHemoglobinThalassemiaAnemiaOxygen–haemoglobin dissociation curveHemoglobin EHemoglobin FMedicineBeta thalassemiaInternal medicineGastroenterologyFetal hemoglobinBiologyGenetics

Abstract

fetched live from OpenAlex

Hemoglobin E β thalassemia is the commonest form of severe thalassemia in many Asian countries. Its remarkably variable clinical phenotype presents a major challenge to determining its most appropriate management. In particular, it is not clear why some patients with this condition can develop and function well at very low hemoglobin levels. Here, we demonstrate that patients with hemoglobin Eβ thalassemia have a significant decrease in the oxygen affinity of their hemoglobin, that is an increased P(50) value, in response to anemia. This may in part reflect the lower level of hemoglobin F in this condition compared with other forms of β thalassemia intermedia. The ability to right-shift the oxygen dissociation curve was retained across the spectrum of mild and severe phenotypes, despite the significantly higher levels of hemoglobin F in the former, suggesting that efforts directed at producing a modest increase in the level of hemoglobin F in symptomatic patients with this disease should be of therapeutic value.

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)0.000
metaresearch head score (Gemma)0.000
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: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.008
GPT teacher head0.241
Teacher spread0.233 · 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

Citations29
Published2010
Admission routes1
Has abstractyes

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