Thoracotomy for repair of esophageal atresia: not as bad as they want you to think!
Bibliographic record
Abstract
Is it outdated now to do a thoracotomy to repair esophageal atresia (EA)? Our practices and the literature on the subject of thoracoscopic and open thoracotomy repair of EA were reviewed, seeking answers to the following questions: Is it correct to compare the new thoracoscopic approach for the repair of EA against the thoracotomy techniques of 15-30 years ago? Should post-thoracotomy scoliosis/thoracic deformity reported in up to 56% of patients be a significant current concern? Are the clips used to close the fistula in thoracoscopic repairs as safe as open suture closures? Is the leak and stricture rate similar with thoracoscopic surgery? Are the anesthesia, period of ventilation, pain, time to first feeding, and the length of hospital stay significantly different with current thoracotomy techniques compared with thoracoscopic methods? Is the cosmetic result of a thoracoscopic repair significantly better? Is the learning curve for EA thoracoscopic repair harming patients for minimal long-term benefit? These questions were scientifically unanswerable at this time. The limited EA thoracotomies currently performed have a track record of proven safety and minimal morbidity. The results published by surgeons who are pioneers in thoracoscopy may not be generalizable, and the complication rate from teams with less experience is likely underreported. In selected patients and with experienced teams, thoracoscopic EA repair is appropriate. However, EA repair via thoracotomy should, for now, remain as the 'gold standard'. Further registry-based, multicenter, comparative studies on EA repair methodologies and outcomes should provide important answers.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 0.005 |
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".