Fetal Alert Network: An integrated population-based fetal care network - Linking conception to outcome.
Bibliographic record
Abstract
BACKGROUND: Fetal diagnosis and intervention are changing the nature and natural history of many congenital anomalies treated today. Given the small cohort numbers and complexity of many congenital anomalies, an integrated network of accurate and timely information sharing is essential for evidence-based best clinical practice and outcomes. METHODS: A prospective, population-based, real-time provincial database was established by a transdisciplinary team of antenatal care providers consisting of five perinatal centres and affiliated paediatric subspecialties providing care. RESULTS: Eight hundred thirty-two pregnant women referred for fetal anomalies were registered between April 2, 2005, and March 31, 2006. The women had a mean +/- SD age of 30+/-6.2 years, and approximately 90% of the pregnancies were spontaneously conceived. In addition, more than 90% of the women had no predisposing genetic history. The mean time of initial diagnosis was 21 weeks gestational age, and the mean time of referral was 24.7 weeks gestational age. Approximately 50% of the patients had no antenatal screening. Detailed analysis such as geographical mapping demonstrated regional differences in fetal anomalies prevalence, practice differences and clinical outcomes. CONCLUSIONS: Accurate, precise and real-time collection of fetal care and health systems utilization information establishes a new benchmark, and reveals some critical deficiencies such as lack of antenatal care and delayed referral for fetal anomalies in Ontario.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".