Functional Luminal Imaging Probe in the Management of Pediatric Esophageal Disorders
Bibliographic record
Abstract
BACKGROUND: Functional luminal imaging probe (FLIP) measures pressure-geometry relationships of digestive luminal space. When used in esophageal disorders, it provides several luminal parameters that help better understand the pathophysiology. Data about the potential utility of FLIP in pediatrics are scarce and there is no standardized use in children. We aim to describe the use of FLIP in our center, its safety, feasibility, and clinical impact in esophageal disorders in children. METHODS: Consecutive FLIP recordings performed at the Centre Hospitalier Universitaire-Sainte-Justine, Montréal, Canada between February 2018 and January 2021 were extracted. A chart review was conducted for demographics and medical history. Symptomatology after the procedure was evaluated with validated dysphagia scores. KEY RESULTS: Nineteen patients were included (11 girls, median age 16 years, range 3.2-19.6) with achalasia (n = 5), post-Heller's myotomy dysphagia (n = 3), esophagogastric junction outflow obstruction (n = 3), congenital esophageal stenosis (n = 2); post-esophageal atresia repair stricture (n = 3), and post-fundoplication dysphagia (n = 3). There was no significant correlation between integrated relaxation pressure measured with high resolution manometry and distensibility index (DI). The use of FLIP made it possible to differentiate between dysphagia related to an esophageal obstruction (DI < 2.8 mm2/mmHg) and dysphagia without major motility disorder (DI > 2.8 mm2/mmHg) that guided the indication for dilation. FLIP led to a change in management in 47% of the patients. Forty-seven percent of the patients were symptom free at the time of the evaluation. CONCLUSIONS INFERENCES: FLIP provides key esophageal luminal values and therefore can play an important role in pediatric esophageal disorders management.
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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 | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".