Predictors of poor outcomes in children with tracheoesophageal fistula/oesophageal atresia: an Australian experience
Bibliographic record
Abstract
Objective: The aim of this study is to characterize long-term morbidities of oesophageal atresia (OA) with or without tracheoesophageal fistula (TOF). Methods: Infants born with OA/TOF from 2000 to 2016 in Western Australia were included for analysis. Infants were categorized into high-risk and low-risk groups based on the presence of one or more perioperative risk factors [low birth weight, vertebraldefects, anal atresia, cardiac defects, TOF, renalanomalies, limb abnormalities (VACTERL), anastomotic leak, long gap OA, and failure to establish oral feeds within the first month] identified by a previous Canadian study. Frequency of morbidities in infants with perioperative risk factors was compared. Results: Of 102 patients, 88 (86%) had OA with distal TOF (type C). The most common morbidities in our cohort were anastomotic oesophageal strictures (AS) (n=53, 52%), tracheomalacia (n=48, 47%), gastroesophageal reflux disease (GORD) (n=42, 41%) and recurrent respiratory tract infections (n=40, 39%). Presence of GORD (30/59 vs 12/43, p=0.04) and median frequency of AS dilatations (8 vs 3, n=59, p=0.03) were greater in the high-risk group. This study further confirmed that inability to be fed orally within the first month was associated with high morbidities. Conclusions: Gastrointestinal and respiratory morbidities remain high in OA/TOF regardless of perioperative risk factors. Inability to be fed orally within the first month is a predictor of poor outcomes with high frequency of gastrointestinal and respiratory comorbidities.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| 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".