Learning curves of open and endoscopic fetal spina bifida closure: systematic review and meta‐analysis
Bibliographic record
Abstract
OBJECTIVE: The Management of Myelomeningocele Study (MOMS) trial demonstrated the safety and efficacy of open fetal surgery for spina bifida aperta (SBA). Recently developed alternative techniques may reduce maternal risks without compromising the fetal neuroprotective effects. The aim of this systematic review was to assess the learning curve (LC) of different fetal SBA closure techniques. METHODS: MEDLINE, Web of Science, EMBASE, Scopus and Cochrane databases and the gray literature were searched to identify relevant articles on fetal surgery for SBA, without language restriction, published between January 1980 and October 2018. Identified studies were reviewed systematically and those reporting all consecutive procedures and with postnatal follow-up ≥ 12 months were selected. Studies were included only if they reported outcome variables necessary to measure the LC, as defined by fetal safety and efficacy. Two authors independently retrieved data, assessed the quality of the studies and categorized observations into blocks of 30 patients. For meta-analysis, data were pooled using a random-effects model when heterogeneous. To measure the LC, we used two complementary methods. In the group-splitting method, competency was defined when the procedure provided results comparable to those in the MOMS trial for 12 outcome variables representing the immediate surgical outcome, short-term neonatal neuroprotection and long-term neuroprotection at ≥ 12 months of age. Then, when raw patient data were available, we performed cumulative sum analysis based on a composite binary outcome defining successful surgery. The composite outcome combined four clinically relevant variables for safety (absence of extreme preterm delivery < 30 weeks, absence of fetal death ≤ 7 days after surgery) and efficacy (reversal of hindbrain herniation and absence of any neonatal treatment of dehiscence or cerebrospinal fluid leakage at the closure site). RESULTS: Of 6024 search results, 17 (0.3%) studies were included, all of which had low, moderate or unclear risk of bias. Fetal SBA closure was performed using standard hysterotomy (11 studies), mini-hysterotomy (one study) or fetoscopy by either exteriorized-uterus single-layer closure (one study), percutaneous single-layer closure (three studies) or percutaneous two-layer closure (one study). Only outcomes for standard hysterotomy could be meta-analyzed. Overall, outcomes improved significantly with experience. Competency was reached after 35 consecutive cases for standard hysterotomy and was predicted to be achieved after ≥ 57 cases for mini-hysterotomy and ≥ 56 for percutaneous two-layer fetoscopy. For percutaneous and exteriorized-uterus single-layer fetoscopy, competency was not reached in the 81 and 28 cases available for analysis, respectively, and LC prediction analysis could not be performed. CONCLUSIONS: The number of cases operated is correlated with the outcome of fetal SBA closure, and the number of operated cases required to reach competency ranges from 35 for standard hysterotomy to ≥ 56-57 for minimally invasive modifications. Our observations provide important information for institutions looking to establish a new fetal center, develop a new fetal surgery technique or train their team, and inform referring clinicians, potential patients and third parties. Copyright © 2019 ISUOG. Published by John Wiley & Sons Ltd.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.025 | 0.070 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.039 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".