Clinical Outcomes that Fetal Surgery for Myelomeningocele Needs to Achieve
Bibliographic record
Abstract
INTRODUCTION: The development of techniques to close open neural tube malformations prior to birth has generated great interest and hope for fetal interventions and their outcomes. To plan a randomized trial, as is being discussed at three centres in the United States, the determination of what constitutes a clinically significant improvement in outcome is critical. To date, preliminary observations from two centres suggest that improvements may occur, not in spinal cord function as originally postulated, but in the extent of the hindbrain hernia and the frequency that shunting is required to control hydrocephalus. PURPOSE: The determination of what outcome would constitute an important and clinically significant difference in outcome to be achieved by fetal intervention for myelomeningocele. METHOD: Parents of patients and patients treated in our myelomeningocele clinic were surveyed using a structured and validated tool. From the perspective of a recommendation to a close friend or family member, the interviewees were asked to quantify on a scale from 0 to 100 the chance of specific outcomes (need for a shunt, need for a wheelchair, change of urinary incontinence) that a fetal operation would need to predictably achieve. RESULTS: Responses were obtained from 77 patients/families. The fifty percentile response in each study dimension was as follows: the chance of needing a shunt was 12 % (range 0 - 50 %), the chance of needing a wheelchair was 8 % and the chance of being incontinent was 5 % (range 0 - 25 %). CONCLUSIONS: Fetal interventions will have to achieve significant improvements in the control of hydrocephalus, mobilization and continence over postnatal treatment to be justified.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.005 | 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".