Bibliographic record
Abstract
PURPOSE OF REVIEW: The diagnosis and treatment of gastroschisis spans the perinatal disciplines of maternal fetal medicine, neonatology, and pediatric surgery. Since gastroschisis is one of the commonest and costliest structural birth defects treated in neonatal ICUs, a comprehensive review of its epidemiology, prenatal diagnosis, postnatal treatment, and short and long-term outcomes is both timely and relevant. RECENT FINDINGS: The incidence of gastroschisis has increased dramatically over the past 20 years, leading to a renewed interest in causation. The widespread availability of maternal screening and ultrasound results in very high rates of prenatal diagnosis, which enables evaluation of the optimal timing and mode of delivery. The preferred method of surgical closure continues to be an issue of debate among pediatric surgeons, whereas postsurgical treatment seeks to expedite the initiation and progression of enteral feeding and minimize complications. A small subset of babies with complex gastroschisis leading to intestinal failure benefit from the knowledge and expertise of dedicated interdisciplinary teams, which seek to bring novel therapies and improved clinical outcomes. SUMMARY: The opportunities to increase the knowledge of causation, and identify best practices leading to improved outcomes, drive the ongoing need for collaborative clinical research in gastroschisis.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".