Fetal myelomeningocele surgery: Only treating the tip of the iceberg
Bibliographic record
Abstract
OBJECTIVE: Fetal myelomeningocele (fMMC) surgery improves infant outcomes when compared with postnatal surgery. Surgical selection criteria and the option of pregnancy termination, however, limit the number of cases that are eligible for prenatal surgery. We aimed to quantify what proportion of cases could ultimately benefit from fetal therapy. METHODS: We retrospectively reviewed all cases of fMMC referred to a large tertiary care center over a 10-year period and assessed their eligibility for fetal surgery, pregnancy termination rates, and actual uptake of the surgery. RESULTS: Of 158 cases, 67 (42%) were ineligible for fetal surgery based on surgical exclusion criteria. Eleven fetuses (7%) had chromosomal anomalies, 10 of which (91%) had other anomalies on ultrasound. Thirty-four patients had a combination of maternal and fetal contraindications. Of the remaining 91 eligible cases (58%), 45 (49%) pregnancies were terminated, leaving only 46 (29% of initial 158 cases) as potential candidates for fetal repair. Actual uptake of fetal surgery was 15% (n = 14 of 91), but this increased after a national program was started. CONCLUSION: Only a minority of fMMC cases will ultimately undergo fetal surgery. These numbers support the centralization of care in expert centers.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".