Paciente con regurgitación: cómo estudiarla y cuáles son sus principales diagnósticos diferenciales
Bibliographic record
Abstract
In gastroenterological practice, gastroesophageal reflux disease is one of the most frequent diagnoses. In this article the potential confounding of gastroesophageal reflux will be raised. According to the Montreal definition, “is a condition that develops when the reflux of stomach contents into the esophagus causes troublesome symptoms and/or complications.” However, it is becoming increasingly clear that sometimes symptoms suspected to be caused by gastroesophageal reflux disease are the expression of other functional and behavioral disorders or even structural lesions. From this complexity arise reflux confounding, where rumination and supragastric belching may present symptoms similar to gastroesophageal reflux disease, be initially treated with the proton pump inhibitor-based guidelines, and thus be mistakenly targeted. Likewise, regurgitation may be the symptomatic expression of different functional disorders and not exclusively a “typical” symptom of gastroesophageal reflux disease. Hence the need and the challenge for the treating physician to correctly identify the pathophysiological mechanisms responsible for the patient’s symptoms for a correct therapeutic approach.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".