Esophageal atresia — state of the art (review of literature on the PUBMED base)
Bibliographic record
Abstract
Esophageal atresia is fundamentally important in pediatric surgery, as its treatment results create an accurate portrait of the standards of surgical care, which is provided by any medical institution. Cameron Haight performed the first successful reconstruction of the esophagus malformation relatively recently just over 70 years ago. Advances in neonatal surgery, technology and advances in neonatal intensive care have dramatically improved treatment outcomes for this disease. Current mortality from this developmental abnormality has become minimal, despite its frequent association with other malformations. A revolutionary approach to the treatment of esophageal atresia was developed by Steven Rothenberg in 1999, the pioneer and legend of pediatric minimally invasive surgery. In 1999, he revolutionized esophageal anastomosis by thoracoscopy. The innovative concept of the minimally invasive approach ensured minimal trauma to patients, thereby providing the possibility of rapid postoperative recovery of patients and reducing the length of their surgical hospital stay. This literature review raises the main controversy in the surgical treatment of esophageal atresia in the 21st century. Emphasis is placed on the study of the role of minimally invasive surgery and the treatment of concomitant abnormalities associated with gastroesophageal reflux and tracheomalacia. As part of the study, a description of all the latest technologies, advances in technical and scientific terms, solutions, exceptional experience, and competence in developing and implementing minimally invasive operations in young children. These developments changed the direction of pediatric surgery and improved the quality of surgical care for children with congenital malformation of the esophagus and how they were performed.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.014 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".