Genotype–phenotype correlation in children with hereditary spherocytosis
Bibliographic record
Abstract
Hereditary spherocytosis (HS) is a common inherited haemolytic anaemia attributed to disturbances in five different red cell membrane proteins. We performed a retrospective study of 166 children with HS and describe the clinical phenotype according to the genotype. In 160/166 (97%) children with HS a disease-causing mutation was identified. Pathogenic variants in ANK1, SPTB, SLC4A1 and SPTA1 were found in 49%, 33%, 13% and 5% of patients. Children with SLC4A1-HS had the mildest phenotype, showing the highest haemoglobin (P < 0·001), lowest reticulocyte counts (P < 0·001) and lowest unconjugated bilirubin levels (P = 0·006), and none required splenectomy in childhood (P < 0·001). Conversely, children with autosomal recessive SPTA1-HS had the most severe clinical phenotype, with almost all patients undergoing splenectomy in early childhood. Patients with ANK1 and SPTB variants showed a similar clinical phenotype. Within each gene, variant type or location did not predict disease severity or likelihood of splenectomy. Among patients with a genetic diagnosis, 47 (29%) underwent splenectomy (23 partial; 24 total) while 57 (36%) underwent cholecystectomy. Total splenectomy led to greater improvements in haemoglobin (P = 0·02). Select use of genetic testing (especially in patients without a family history) may help predict clinical phenotype in childhood and guide family counselling.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".