Design of Predictive Models for Positive Outcomes of Upper and Lower Gastrointestinal Endoscopies in Children and Adolescents
Bibliographic record
Abstract
OBJECTIVES: To develop models to accurately determine the outcomes of diagnostic endoscopies performed in children and adolescents without known gastrointestinal disease. MATERIALS AND METHODS: Retrospective chart review of all endoscopies performed in children 2 to 18 years of age without known gastrointestinal disease from January 1 to December 31, 2000. The association between age, presenting symptoms, physical examination findings, laboratory investigations, and endoscopy outcomes was assessed. Predictive models for positive outcomes on endoscopy were estimated for upper and lower endoscopies separately by use of multiple logistic regression. Receiver operating curves were constructed to evaluate the performance of the models. A model with a sensitivity of 95% and specificity of 40% was considered clinically significant. RESULTS: Positive findings on endoscopy were found in 191 (55%) of 346 and in 120 (59%) of 204 upper and lower endoscopies, respectively. Age above 13 years, vomiting, and hypoalbuminemia were significant predictors of positive upper endoscopies. Rectal bleeding, hypoalbuminemia, and elevated erythrocyte sedimentation rate were significant predictors of positive lower endoscopies. Extrapolating from the receiver operating curves, a sensitivity of 95% corresponded to a specificity of 10% for the upper endoscopy model and 30% for the lower endoscopy model. CONCLUSIONS: In our population of children and adolescents, several clinical characteristics were predictive of positive upper and lower endoscopy outcomes. Predictive models composed of these clinical variables were statistically, but not clinically, significant. The inclusion of additional clinical characteristics that could be assessed in prospective studies will likely improve the clinical significance of endoscopy outcome prediction.
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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 | 0.033 | 0.058 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".