Factors Predictive of a Poor Outcome in Patients with Esophageal Atresia
Bibliographic record
Abstract
Purpose: Despite a major improvement in the survival rate of patients with esophageal atresia and tracheoesophageal fistula (EA-TEF), the morbidity associated with this condition remains high. Given this, the aim of our study was to identify early predictive factors of serious complications during the 1st year of life (short term) and after 1 year of age (long term) in these patients. Methods: We retrospectively reviewed the charts of all EA-TEF children born between January 1990 and May 2005. Included cases had been followed at Hopital Sainte-Justine for a minimum of one year. Variables analyzed included different patient characteristics, details of the surgery and early post-operative period. A complicated evolution was defined as the occurrence of at least one of severe gastroesophageal reflux, esophageal stricture needing dilatation, recurrent fistula needing surgery, need for gavage feeding for ≥ 3 months, esophageal foreign body impaction needing endoscopic removal, severe tracheomalacia, chronic respiratory disease and death. Results: 152 EA-TEF patients fulfilled our inclusion criteria. 47% were female. Mean gestational age was 36.8 weeks (24–42 wks) and mean birth weight was 2515 g (555–4334 g). 47% had a complicated evolution before one year of age and 45% after one year. Multiple logistic regression demonstrated in the first year of life, 4 groups of significant variables (P≤ 0.05) associated with a complex evolution: Surgery associated issues (long gap, 2 step surgery, Foker, initial gastrostomy, anastomotic leak))[OR 5.4; 95% CI 1.7–17], Post-op respiratory complications (pneumothorax, intubation ≥ 5 days p-op, chylothorax) [OR 4.4; 95% CI 1.6–12], Post-op feeding difficulties (Inability to feed orally, need for gavage feeding or gastrostomy) [OR 2.6; 95% CI 1.1–6.1], “other” malformation not related to VACTERL [OR 2.2; 95% CI 1.0–5.0]. After one year of age, only the surgery associated issues[ OR 2.8; 95% CI 1.4–5.9] and a complicated evolution in the first year of life were predictive of a continued complicated evolution [OR 3.6; 95% CI 1.4–9.2]. Conclusion: In children with EA-TEF, we found a number of variables predictive of a high risk of morbidity during the first year of life. Using these variables to identify EA-TEF children at higher risk of complications will allow us to modify their medical management and hopefully reduce their long term morbidity.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".