The Ever-Changing Landscape of Drug-Induced Injury of the Lower Gastrointestinal Tract
Bibliographic record
Abstract
CONTEXT: -There is an ever-growing armamentarium of pharmacologic agents that can cause gastrointestinal (GI) mucosal injury, the most common symptoms being diarrhea, constipation, nausea, and vomiting. These are often self-limiting and without serious sequelae, but some symptoms are of greater concern, like drug-induced mucosal ulceration that can manifest as GI hemorrhage, stricture formation, and even perforation. Histologically, there is significant overlap between drug-induced injuries and various disease entities. A single type of medication may cause multiple patterns of injury, which can involve the entire GI tract or just some parts of it. OBJECTIVE: -To review the most common drug-induced injury patterns affecting the colon, which may be recognized by the surgical pathologist on colonic mucosal biopsies. This review does not address the injuries occurring in the upper GI tract. DATA SOURCES: -A PubMed review of English-language literature, up to December 2015, on drug-induced injury of GI tract was performed. CONCLUSIONS: -There are numerous drugs that damage the colonic mucosa. The most common drugs are included in this review according to their histologic pattern of injury. It is important for the pathologist to keep in mind that a single drug type can induce many histologic patterns of mucosal injury that can mimic many disease entities. Although there are some histologic clues helpful in the diagnosis of drug-induced colonic injury, correlation with clinical history and especially medication history is essential to improve diagnostic accuracy.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.000 |
| 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".