Sickle Cell Disease as a Multifactorial Condition
Bibliographic record
Abstract
Abstract The phenotype of sickle cell anaemia is heterogeneous. Although all patients have the identical sickle cell mutation, the type, severity and frequency of complications is variable. The products of epistatic modifying genes and the sickle haemoglobin gene, along with environmental influences, interact to determine the disease phenotype. Haemoglobin F concentration and distribution among erythrocytes is likely the most important genetic modulator of sickle cell disease severity. Several genetic loci are associated with haemoglobin F expression, including BCL11A in chromosome 2p, the HBS1L ‐ MYB locus on 6q23, the C‐T polymorphism 5′ to HBG2 on chromosome 11p, and the olfactory receptor genes, OR51B6 and OR51B5 , also on 11p. α‐Thalassaemia is another modulator of sickle cell disease. There is evidence that genes associated with endothelial activation, inflammation, red blood cell hydration and hemostasis might all play a role in phenotypic diversity. Key Concepts: Sickle cell anaemia is a single‐gene disorder with heterogeneous clinical features. The phenotype of sickle cell anaemia is affected by epistatic modifier genes. Haemoglobin F is the best‐known genetic modifier of sickle cell anaemia. Polymorphisms in three established quantitative trait loci modulate haemoglobin F. Co‐inheritance of α‐thalassaemia is associated with reduced rates of haemolysis and vasculopathic complications, but increased incidence of viscosity‐vaso‐occlusive manifestations. Candidate gene and genome‐wide association studies have identified genes that potentially affect sickle cell disease phenotype by modifying disease pathogenesis.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
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".