Newborn screening for sickle cell disease: A 1988–2003 Quebec experience
Bibliographic record
Abstract
BACKGROUND: Sickle cell disease (SCD) is associated with significant mortality and morbidity that can be decreased by neonatal diagnosis. Although 44 American states have implemented such programs, there are no provincially funded universal or targeted newborn screening programs for SCD in Canada. OBJECTIVE: To report a critical appraisal of a hospital-based neonatal screening program targeting at-risk infants over a 15-year period. METHODS: The cord blood of infants born at Sainte-Justine University Health Centre (Sainte-Justine UHC, Montreal, Quebec) whose mother or father was black was collected at birth and analyzed for the presence of hemoglobin (Hb) S by liquid chromatography or isoelectric focusing. Samples with positive results underwent confirmatory testing. RESULTS: A total of 9619 infants were screened: 8142 (84.6%) had a normal phenotype, 1012 (10.5%) had sickle cell trait and 386 (4.0%) had HbC trait. Seventy-two infants were diagnosed with SCD: 37 (0.4%) were classified as HbSS or HbS-beta-thalassemia and 35 (0.4%) had HbSC disease. Of these 72 infants, 67 (93.1%) were immediately enrolled in a multidisciplinary SCD follow-up clinic. The five remaining children not initially enrolled were later referred to the clinic. A chart study revealed that six patients with SCD born at Sainte-Justine UHC were not identified by neonatal screening. CONCLUSIONS: The screening program was clinically effective because it identified 92.3% of at-risk patients born at Sainte-Justine UHC. These infants received appropriate medical care before 10 weeks of age as opposed to a median of 12 months for infants not identified by the screening program. It is proposed that either a targeted or a universal neonatal screening for SCD should be available in Canada.
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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 | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".