The Fetal Alert Network: Surveying congenital anomalies
Bibliographic record
Abstract
To the Editor; Meschino (1) has commented on the innovative nature of the Fetal Alert Network (FAN) in Ontario as was reviewed previously (2); however, we would like to point out that there are only two provinces in Canada that have population-based congenital anomaly (birth defect) registries or surveillance systems, namely British Columbia and Alberta. The remaining provinces that Meschino listed, as did Kim et al (3), have very limited systems such as the maternal serum screen programs of Manitoba and Ontario, perinatal databases of Ontario and Nova Scotia (Nova Scotia also has a fetal anomaly registry) and the Newfoundland provincial medical genetics program with a short ascertainment period. The latter depends on referral to their program and is, therefore, not population-based and would not necessarily have any information on stillbirths or terminations of pregnancy. We agree that it is a serious deficiency not to have a good congenital anomaly surveillance system in Ontario, where 40% of Canada's births take place. The FAN depends on a patient being referred for prenatal diagnosis to one of the five collaborating centres, but they do not state how they will capture cases who are not referred, who are stillborn or who die with a congenital anomaly after the perinatal period. In our opinion, Ontario could use the FAN as an excellent start but they will need to do much more than that if they are to have a comprehensive population-based surveillance system for birth defects. The FAN's one-year study of 832 cases comprises only 0.63% of Ontario's annual births (approximately 132,000) (4). Having a central clearinghouse with the FAN as its base, collecting data from many other sources including the Ontario perinatal system and the maternal serum screen system as well as from genetics clinics, would be a good start. Ontario has also had a problem in the past with the denominator because not all births were registered, but we understand Ontario Vital Statistics is rectifying that. Meschino should realize that ‘traditional surveillance systems’ have not stood still but are, in fact, ascertaining prenatally diagnosed cases and terminations of pregnancy and also have plans to use record linkage methods to perinatal programs. Consequently, data on maternal health, weight, exposure to teratogens and other risk factors can be linked to the congenital anomaly in question. Ethnicity is not necessarily easy to obtain because Canada has decided that it is not to be collected. We also agree with Meschino that congenital anomalies surveillance systems are not adequate if they cannot ascertain prenatally diagnosed cases and terminations, as is the case with the Public Health Agency of Canada's system (Canadian Congenital Anomalies Surveillance System). We submit that the data from the FAN alone will not allow comparisons with other provinces and countries except for a limited number of sentinel anomalies. The longer an ascertainment period exists, the better the data, but a reasonable compromise is a minimum of one year of age.
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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 | 0.010 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".