Hemoglobinopathie in de 21e eeuw: incidentie, diagnose en hielprikscreening
Bibliographic record
Abstract
To determine the incidence of severe haemoglobinopathy, to evaluate the effect of heel prick screening, and to identify those children who do not benefit from this early diagnosis. Prospective descriptive study. Registration of all symptomatic and asymptomatic children who between 2003-2009 were newly diagnosed with the a severe form of a hereditary disorder concerning the formation of the alpha haemoglobin chain (HbH disease), or the beta haemoglobin chain (sickle cell disease or beta thalassaemia major) in the Netherlands. Registration was done by collecting anonymised reports from the Dutch Paediatric Surveillance Unit and TNO, and by additional questionnaires. During the study period, 48 children (range: 36-76) per year were diagnosed with severe haemoglobinopathy. The overall incidence was 2.5 per 10,000 live births. The incidence of sickle cell disease diagnosed by heel prick screening was 2.1 per 10,000 live births and of thalassaemia major 0.6 per 10,000 live births. In 7% of the children with sickle cell disease who were diagnosed without any form of screening, the diagnosis was made on (a life threatening) infection. Twenty-two percent of the children with a severe form of haemoglobinopathy were not born in the Netherlands. The parents of almost half of the children with sickle cell disease originally came from West- or Central Africa. The parents of children with thalassaemia major were mainly from Morocco or various Asiatic countries. The number of children with severe haemoglobinopathy in the Netherlands has trebled since 1992. In order for all children to benefit from early diagnosis and preventive treatment, it is advisable that children who originate from risk areas should be tested for haemoglobinopathy when they first arrive in the Netherlands
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".