Bibliographic record
Abstract
The aim of carrier testing is to identify carrier couples at risk of having offspring with a serious genetic (autosomal recessive) disorder. Carrier couples are offered genetic consultation where their reproductive options, including prenatal diagnosis, are explained. The Ashkenazi Jewish population is at increased risk for several recessively inherited disorders (Tay-Sachs disease, Cystic fibrosis, Canavan disease, Gaucher disease, Familial Dysautonomia, Niemann-Pick disease, Fanconi anemia, and Bloom syndrome). Unlike Tay-Sachs disease, there is no simple biochemical or enzymatic test to detect carriers for these other disorders. However, with the rapid identification of disease-causing genes in recent years, DNA-based assays are increasingly available for carrier detection. Approximately 5% of the world's population carries a mutation affecting the globin chains of the hemoglobin molecule. Among the most common of these disorders are the thalassemias. The global birth rate of affected infants is at least 2 per 1000 (in unscreened populations), with the greatest incidence in Southeast Asian, Indian, Mediterranean, and Middle Eastern ethnic groups. Carriers are detected by evaluation of red cell indices and morphology, followed by more sophisticated hematological testing and molecular analyses. The following issues need to be considered in the development of a carrier screening program: (1) test selection based on disease severity and test accuracy; (2) funding for testing and genetic counselling; (3) definition of the target population to be screened; (4) development of a public and professional education program; (5) informed consent for screening; and (6) awareness of community needs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".