Sikkelcelziekte in de hielscreening I : opgespoorde kinderen in het eerste jaar
Bibliographic record
Abstract
OBJECTIVE: Evaluation of the results of the first year following the expansion of the Dutch national heel prick screening programme to include sickle cell disease (SCD). DESIGN: Prospective national registration of children with suspected SCD or another type of severe haemoglobinopathy. METHODS: The bloodspots were analysed using a haemoglobin separation method based on high performance liquid chromatography (HPLC). Children with an abnormal heel prick result were supposed to be referred to a paediatric haematologist in 1 of the 8 university hospitals. Confirmation of the diagnosis was made by means of a second HPLC test, a DNA test and an investigation for haemoglobinopathy in the parents. The final diagnosis was compared with the probable diagnosis made during screening. RESULTS: In the first year, 64 children had an abnormal heel prick screening result which indicated a haemoglobinopathy (prevalence: 0.035%). The probable diagnosis was SCD in 41 children, alpha-thalassaemia in 18 children, beta-thalassaemia in 4 children and another form of haemoglobinopathy (HbEE) in 1 child. The probable diagnosis was confirmed in all of the children. CONCLUSION: The first year of the Dutch neonatal screening programme for SCD was successful, considering that the number of children in whom a diagnosis was made was in line with that expected. The positive predictive value of the heel prick result was thus 100%. It is too early to comment on possible false negative test results
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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 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".