THE BEZOARS: CLASSIFICATION, DIAGNOSIS, TREATMENT AND CLINICAL CASES - REVIEW
Bibliographic record
Abstract
Introduction: Bezoars are foreign bodies found mainly in the gastrointestinal tract (but not exclusively) giving non-specific and sparse symptoms, which can contribute to serious complications such as chronic mucositis, intestinal obstruction, peritonitis, sepsis and in some cases can even lead to death. A distinction can be made between trichobezoars, phytobezoars, lactobezoars, pharmacobezoars and urinary tract bezoars. Endoscopic examination plays a major role in diagnosis.[1] Objective: The aim of this review is to provide information on the origin, classification, diagnosis and treatment of bezoars and to present several clinical cases involving different types of bezoars. Methods and materials: For the literature review, a Pubmed database and Polish websites of renowned medical journals were used, using keywords such as: bezoar, trichobezoar, phytobezoar, lactobezoar, pharmacobezoar.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".