Contemporary management and outcomes for infants born with oesophageal atresia
Bibliographic record
Abstract
BACKGROUND: Reports on the management and outcome of rare conditions, such as oesophageal atresia, are frequently limited to case series reporting single-centre experience over many years. The aim of this study was to identify all infants born with oesophageal atresia in the UK and Ireland to describe current clinical practice and outcomes. METHODS: This was a prospective multicentre cohort study of all infants born with oesophageal atresia and/or tracheo-oesophageal fistula in 2008-2009 in the UK and Ireland to record current clinical management and early outcomes. RESULTS: A total of 151 infants admitted to 28 paediatric surgical units were identified. Some aspects of perioperative management were universal, including oesophageal decompression, operative technique and the use of transanastomotic tubes. However, there were a number of areas where clinical practice varied considerably, including the routine use of perioperative chest drains, postoperative contrast studies and antireflux medication, with each of these being employed in 30-50 per cent of patients. There was a trend towards routine postoperative ventilation. CONCLUSION: The prospective methodology used in this study can help identify practices that all surgeons employ and also those that few surgeons use. Areas of clinical equipoise can be recognized and avenues for further research identified.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".