A scoring system to predict mortality in infants with esophageal atresia
Bibliographic record
Abstract
Esophageal atresia (EA) is a rare anomaly that mandates surgical intervention. Patients with EA often have complicated medical courses due to both esophageal anomalies and related comorbidities. Although several prognostic classification systems have been developed to decrease the mortality rate in EA, most systems focus only on the influence of the major anomaly, and external risk factors that could be influenced by the neonatal caregivers to a certain extent are not included. The aim of this study was to investigate the risk factors for in-hospital mortality in neonates with EA and develop a scoring model to predict mortality.In total, 198 infants with EA who were treated with surgical intervention at the Children's Hospital of Chongqing Medical University between March 2004 and June 2016 were included. The demographic information, clinical manifestations, laboratory testing, and outcomes during hospitalization were analyzed retrospectively. A predictive scoring model was developed according to the regression coefficients of the risk factors.The mortality rate was 18.1% (36/198). In the univariate analysis, higher incidences of prematurity, low birth weight, long gap, anastomotic leak, respiratory failure, postoperative sepsis, respiratory distress syndrome, pneumothorax, and septic shock were found in the nonsurvivor group than in the survivor group (P < .05). In the logistic regression analysis, anastomotic leak (OR: 10.75, 95% CI: 3.113-37.128), respiratory failure (OR: 4.104, 95% CI: 2.292-7.355), postoperative sepsis (OR: 3.564, 95% CI: 1.516-8.375), and low birth weight (OR: 8.379, 95% CI: 3.357-20.917) were associated with a high mortality rate. A scoring model for predicting death was developed with a sensitivity of 0.861, a specificity of 0.827, a positive predictive value of 0.524, and a negative predictive value of 0.963 at a cutoff of 2 points. The area under the receiver-operating characteristic curve of the score was 0.905 (95% CI, 0.863-0.948, P = .000) for death from EA. The mortality rate increased rapidly as the scores increased, and all patients with scores ≥5 died.Anastomotic leak, respiratory failure, postoperative sepsis, and low birth weight are independent risk factors for mortality in EA. Infants with a predictive score of 5 had a high risk of death.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".