Bibliographic record
Abstract
This chapter presents information on etiology/pathophysiology, signalment/history, clinical features, differential diagnosis, diagnostics and therapeutics of regurgitation in cats and dogs. Any disease which affects the motility of the esophagus or prevents passage of ingesta through the esophagus or stomach may lead to regurgitation. Regurgitation may be seen due to congenital diseases including primary megaesophagus. Suggested breed predispositions include Irish setter, Great Dane, German shepherd, Labrador retriever, Chinese shar-pei, Newfoundland, miniature schnauzer, fox terrier dogs, and Siamese cats. The approach to the diagnosis of underlying etiology of regurgitation may be multifaceted. Thoracic radiographs are a crucial diagnostic for regurgitating patients. Radiographs may demonstrate an enlarged, air- or food-filled esophagus suggestive of megaesophagus. In dyspneic patients, being able to repeatedly find a dilated esophagus may be necessary to rule out transient aerophagia. Controlling regurgitation is crucial to avoid aspiration pneumonia. Underlying conditions should be treated appropriately.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.140 | 0.078 |
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".