Retrospective analysis of fetal vertebral defects: Associated anomalies, etiologies, and outcome
Bibliographic record
Abstract
Our objectives were to describe fetal cases of vertebral defects (VD), assess the diagnostic yield of fetal chromosomal analysis for VD and determine which investigations should be performed when evaluating fetal VD. We performed a retrospective chart review for fetuses with VD seen between 2006 and 2015. Cases were identified from CHU Sainte-Justine's prenatal clinic visits, postmortem fetal skeletal surveys, and medical records. Cases with neural tube defects were excluded. Sixty-six fetuses with VD were identified at a mean gestational age of 20 weeks. Forty-seven (71.2%) had associated antenatal anomalies, most commonly genitourinary, skeletal/limb, and cardiac anomalies. Thirteen mothers (19.7%) had pregestational diabetes (95% CI [10.1%-29.3%]). Fifty-three cases had chromosomal analysis. Three had abnormal results (5.6%): trisomy 13, trisomy 22, and 9q33.1q34.11 deletion. Thirty-four (51.5%) pregnancies were terminated, one led to intrauterine fetal demise and 31 (46.9%) continued to term. Of 27 children who survived the neonatal period, 21 had congenital scoliosis and 3 had spondylocostal dysostosis. Seven had developmental delay. In conclusion, prenatal evaluation of fetuses with VD should include detailed morphological assessment (including fetal echocardiogram), maternal diabetes screening, and chromosomal microarray if non-isolated. Our findings provide guidance about management and counseling after a diagnosis of fetal VD.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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 | 0.000 |
| 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".