Bibliographic record
Abstract
Historically, Ontario has lagged well behind other jurisdictions in Canada in its ability to survey congenital anomalies within the province. Birth defect registries or programs exist in several provinces, namely, British Columbia, Alberta, Manitoba, Prince Edward Island, Nova Scotia and Newfoundland. While Ontario does track cases of Down syndrome, neural tube defects (NTDs) and trisomy 18 through its Maternal Serum Screening database, it has not had a province-wide program to survey birth defects until recently. Therefore, Dr Kim and other members of the Fetal Alert Network (FAN) (1) of Ontario are to be applauded for taking the first step in developing a birth defects registry that goes beyond the traditional model, by aiming to track anomalies detected before birth and linking them to postnatal outcomes. This approach recognizes the huge advances in prenatal detection and diagnosis of birth defects that have taken place in recent years. Fetal anomaly data will be critical in analyzing the effectiveness of various modalities that result in either a reduction in the incidence of birth defects or an improvement in health outcomes. Analysis of birth defect data without comparable data from prenatally diagnosed cases may lead to some false conclusions. A case in point: efforts to determine the relative contributions of primary versus secondary prevention of NTDs in British Columbia have shown that the declining birth rate of NTDs in the late 1990s was not primarily due to prenatal folic acid supplementation and food fortification, as might have been assumed before the study. Instead, the data showed that enhanced detection of NTDs by maternal serum screening and prenatal ultrasound was responsible for the decline (2). Surveys of prenatally diagnosed cases will also aid in determining prognostic factors for many congenital anomalies, as well as the effectiveness of various fetal medical and surgical therapeutic interventions, and the impact of mode, timing and location of delivery on morbidity for infants with birth defects. Similarly, the data could be used to track risk factors such as maternal age, health, ethnic background and socioeconomic status, exposure to teratogens, family history and other risk categories. Why track birth defects? Congenital anomalies are major contributors to paediatric morbidity and mortality, accounting for approximately one in 10 paediatric hospitalizations (3) and 30% of infant deaths (4). The rise in prominence of birth defects in infant mortality statistics appears to be related to a decline in deaths due to other causes, specifically, sudden infant death syndrome, respiratory distress syndrome, sepsis and asphyxia, as a result of public health campaigns and improvements in perinatal care. At the same time, overall infant mortality rates have declined sharply over the past 15 years, primarily due to advances in prenatal diagnosis and pregnancy termination of congenital anomalies (4). Ontario is Canada's most populous province, with approximately 40% of the country's annual births. Clearly, without data from Ontario, Canada-wide statistics would not be possible. While most of the population consists of urbanites, a substantial proportion live in rural settings in geographically diverse regions of the province. As in other areas of the country, prenatal health care providers vary from academic obstetricians in ‘downtown' teaching hospitals to family doctors in small towns to nurse practitioners in remote nursing units in the far north. Increasingly, midwives are also providing obstetrical care. Data derived from the FAN will allow comparisons with other provinces and countries, as well as highlighting regional variations within the province. Such data may be used to determine, for example, if the incidence of specific anomalies varies with geographical location. These data will be critical in planning appropriate resources to allow optimal access to health care, both diagnostic and therapeutic, for the obstetrical patient population. One of the primary goals of the FAN is to gather data on all birth defects in the province. To date, collection of data has been restricted to patients referred to one of five regional perinatal centres, in which the FAN database is housed. The relatively recent introduction of perinatal centres in major centres has provided an important tertiary level service for complex maternal-fetal problems. However, a significant proportion of pregnancies with fetal anomalies are managed outside the perinatal centres. Genetics play a key role in the etiology of congenital anomalies in almost all cases. Whether it is the dominant factor as in chromosomal anomalies and single gene disorders, or one of a series of factors in single birth defects such as cleft lip and palate or congenital heart defects, understanding genetics is necessary to understanding birth defects. Clinical geneticists and genetic counsellors have and will continue to have a primary role in prenatal diagnosis and management of fetal anomalies. Through a network of clinics, both in major centres as well as outreach clinics in small communities throughout Ontario, the genetics community has provided prenatal genetics services for at least 30 years. It will, therefore, be critical for the FAN to access data from each of the genetics centres and clinics to come close to collecting data on the majority of prenatally detected anomalies. Another of the FAN's primary goals is to improve access and coordination of health care service delivery for pregnant women whose babies have birth defects. To achieve this plan, a Web site with contact information for each of the five perinatal centres, complete with downloadable referral forms, is provided. Similar referral forms could be provided for each of the listed genetic centres and clinics as well. In addition, it would be helpful if providers were given guidance on which referrals are best directed to genetics clinics and which referrals should be sent to maternal-fetal medicine clinics. In relation to the access issue, a somewhat surprising finding in the FAN study was the late average gestation time for detection of fetal anomaly (21 weeks) and the even later average gestational age when patients were first assessed (24.7 weeks). At most genetics clinics, fetal anomalies are usually referred before 20 weeks and are seen immediately after referral. Little data are given about the spectrum of anomalies in the study group or whether the almost four-week lag time between first detection of the anomaly and consultation in a perinatal centre is due to a delay in referral or a problem with wait times. The FAN provides an exciting opportunity to survey congenital anomalies, both prenatally and after birth. The FAN will bring together a wide range of experts, all of whom have special knowledge and skills in diagnosing and treating congenital anomalies. In addition to experts in genetics, paediatrics and maternal-fetal medicine, surgeons, radiologists and paediatric pathologists will all undoubtedly play an important role. We look forward to the next phase of the project where attempts will be made to capture the majority of all birth defects identified in pregnancies and newborns 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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".