Changes in fetal prevalence and outcome for trisomies 13 and 18: a population-based study over 23 years
Bibliographic record
Abstract
OBJECTIVE: Changes in prenatal diagnosis and maternal age are likely to have an impact on live born prevalence of trisomies 13 and 18. We investigated trends in diagnosis, prevalence, and survival in these conditions. METHODS: A population-based study of one UK health region in 1985-2007 using a well-established congenital abnormality register. Individual records were reviewed and live birth and maternal age data obtained. RESULTS: Pregnancies with trisomies 13 and 18 increased from 0.08 to 0.23 per 1000 registered births and 0.20 to 0.65 per 1000 registered births, respectively. Prenatal diagnosis increased and was associated with high termination rates. Live born prevalence with trisomy 13 decreased from 0.05 to 0.03 per 1000 live births and with trisomy 18 from 0.16 to 0.10 per 1000 live births. Postnatal survival remains poor: one baby (3%) with trisomy 13 and four (6%) with trisomy 18 survived the first year. The percentage of mothers over 35 years increased from 6 to 15%. CONCLUSIONS: Changes in prenatal screening and maternal age have had dramatic effects on the live born prevalence of trisomies 13 and 18. Infant survival remains largely unchanged with the majority dying in the neonatal period.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".