P-16: The Esophageal Anastomotic Stricture Index (EASI) for The Management Of Esophageal Atresia
Bibliographic record
Abstract
Anastomotic stricture is the most common complication following repair of esophageal atresia. An Esophageal Anastomotic Stricture Index (EASI) based on the post-operative esophagram may identify patients at high risk of stricture formation. Digital images of early post-operative esophagrams of patients undergoing EA repair from 2005–2013 were assessed. Demographics and outcomes including dilations were prospectively collected. Upper (U-EASI) and lower (L-EASI) pouch ratios were generated using stricture diameter divided by maximal respective pouch diameter. Score performances were evaluated with area under the receiver operator curves (AUC) and the Fischer's exact test for single and multiple (>3) dilatations. Inter-rater agreement was evaluated using the intraclass correlation coefficient (ICC). Forty-five patients had esophagrams analyzed; 28 (62%) required dilatation and 19 received >3 (42%). U-EASI and L- EASI ratios ranged from 0.17–0.70, with L-EASI outperforming the U-EASI as follows: L-EASI AUC: 0.66 for a single dilatation, 0.65 for >3 dilatations; U-EASI AUC: 0.56 for a single dilatation, 0.67 for >3 dilatations. All patients with a L- EASI ratio of <0.30 (n = 8) required multiple esophageal dilatations, p = 0.006. The inter-rater ICC was 0.87. The EASI is a simple, reproducible tool to predict the development and severity of anastomotic stricture after esophageal atresia repair and can direct postoperative surveillance.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".