Pregnancy outcome in women assigned an ICD‐9/ICD‐9‐CM 655.0 code “central nervous system malformation in fetus affecting management of pregnancy”: Can these codes aid in surveillance for central nervous system defects?
Bibliographic record
Abstract
BACKGROUND: The difficulty many birth defects registries have in identifying early pregnancy terminations can lead to significant underreporting of certain types of malformations. This study was designed to determine the potential benefit of using maternal ICD-9 codes relating to management of pregnancy due to fetal anomaly in surveillance of central nervous system (CNS) defects (655.0). METHODS: Women assigned the ICD-9/ICD-9-CM (henceforth termed ICD-9) code 655.0 between 1990 and 2000 were ascertained from two administrative health databases in Manitoba, Canada, one relating to maternal serum screening and one based on hospital discharges. Information was collected on the type of CNS defect identified and whether it was isolated or associated with other anomalies. The relationship of these variables to pregnancy outcome and its potential impact on surveillance for these types of defects was explored. RESULTS: Neural tube defects were the most common finding (67%) identified in the fetuses/infants of women assigned code 655.0, followed by hydrocephaly (20%) and Dandy-Walker anomaly (6%), but the proportion of neural tube defect cases declined after food fortification with folic acid. Termination of pregnancy occurred in ∼50% of neural tube defect cases, 18% before 20 weeks of gestation, but was rare for other CNS defects. CONCLUSION: The maternal ICD-9 code 655.0 had a very high (97%) positive predictive value for identifying fetuses with a CNS defect; 655.00 was particularly effective in identifying neural tube defects pregnancies terminated before 20 weeks gestation that would be difficult to ascertain, especially by passive surveillance systems using infant related data alone.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".