P-28: Quality of Reporting of the Literature on Gastrointestinal Reflux After Repair of Esophageal Atresia-Tracheoesophageal Fistula
Bibliographic record
Abstract
There is variation in the management of postoperative gastroesophageal reflux (GER) in esophageal atresia-tracheoesophageal fistula (EA-TEF). Well-reported literature is important for clinical decision-making. We assessed the quality of reporting (QOR) of postoperative GER management in EA-TEF. A comprehensive search of MEDLINE, EMBASE, CINHAL, CENTRAL databases and grey literature was conducted. Included articles reported a primary diagnosis of EA-TEF, a secondary diagnosis of postoperative GER, and primary treatment of GER with anti-reflux medications. The QOR was assessed using the STrengthening the Reporting of OBservational studies in Epidemiology (STROBE) checklist. An overall quality percentage (OQP) score was calculated. Retrieval of 2910 articles resulted in 48 relevant articles (N = 2592 patients) with an OQP of 48–95% (median = 65%). The best reported items were “participants and outcome data” (93.8%), “general results” (91.7%) and “background/descriptive data” (89.6%). Less than 20% of studies provided detailed “main results;” less than 5% of studies reported adequately on “bias” or “funding.” Sample size calculation and study limitations were included in 17 (35.4%) and 16 (33.3%) studies respectively. Follow-up time was inconsistently reported. Although the overall QOR is moderate using STROBE, important areas are under-reported. Inadequate methodological reporting may lead to inappropriate clinical decisions. Awareness of STROBE emphasizing proper reporting is needed.
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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 | 0.017 | 0.126 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.023 | 0.020 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".