The UCD Experience with Classic Gastric Emptying Scintigraphy – Systematic Review and Meta-analysis
Bibliographic record
Abstract
Purpose: Gastric emptying scintigraphy (GES) is the gold standard for diagnosing gastroparesis. We retrospectively investigate the largest number of GES at a single institute using the classic protocol. The gastric emptying half-time (T 50 ) is evaluated with factors that may alter the half-time. Method: 515 solid GES were compared with the patient’s medical history, drugs used before exam, gender, body mass index (BMI) at the time of exam, indication for the exam, and age. After consumption of Technetium-99m albumin colloid, anterior and posterior images were acquired in 15 minutes intervals for at least 90 minutes. The half-time was linearly or exponentially interpolated on the basis of multiple data points using software. Results : The average normal T 50 was 72.8 minutes ± 50.2 minutes. T 50 increased in patients with a medical history of diabetes (p < 0.042), hiatal hernia, gastrointestinal obstruction, esophagitis, use of narcotic (p < 0.003), and a BMI under 18.5. It did not have any correlation with gender. T 50 decreased with increasing age or use of metoclopramide (p < 0.0005). Conclusion: GES is an important diagnostic step that leads to the correct treatment. T 50 is an easy and objective value for diagnosing gastroparesis. It is associated with many diseases that nuclear physicians and radiologists should be aware of.
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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 | 0.009 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.014 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".