A population‐based profile of 160 Australians with Prader‐Willi syndrome: Trends in diagnosis, birth prevalence and birth characteristics
Bibliographic record
Abstract
Perceived temporal trends in recognition and diagnosis of Prader-Willi syndrome served as the rationale for an updated epidemiological profile of individuals with this syndrome. Data from the Victorian Prader-Willi Syndrome Register were used to explore birth prevalence, birth characteristics, timing of diagnosis, and molecular mechanism, and to identify trends over time. Maternal age, birth gestation, small for gestational age, and sex were compared across molecular mechanisms. Between 1951 and 2012 there were 160 individuals with Prader-Willi syndrome, known to the Victorian Prader-Willi Syndrome Register, who were born in the Australian state of Victoria. The birth prevalence for individuals with a molecular diagnosis of Prader-Willi syndrome was estimated to be 1:15,830 for 2003-2012. Compared to 1973-1981, the decade 2003-2012 saw an increase in the rate of molecular diagnosis from 58% to 96%, more complete identification of the molecular mechanism (42% vs. 83%), earlier molecular diagnosis (1.3 years vs. 8.6 weeks), and a rise in the relative proportion of maternal uniparental disomy from 0% to 45%. One quarter of infants was born preterm and 53% were small for gestational age. This study confirms a temporal change in diagnostic patterns, suggests a greater relative contribution of maternal uniparental disomy as a molecular mechanism, provides a more robust estimate of birth prevalence and provides evidence of in utero growth restriction for this group. These findings have important clinical and health service delivery implications and pave the way for further research in Prader-Willi syndrome.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".