Ischemic or toxic injury: A challenging diagnosis and treatment of drug-induced stenosis of the sigmoid colon
Bibliographic record
Abstract
A 48-year-old woman was admitted with 15-mo history of abdominal pain, diarrhea and hematochezia, and 5-mo history of defecation difficulty. She had been successively admitted to nine hospitals, with an initial diagnosis of inflammatory bowel disease with stenotic sigmoid colon. Findings from computed tomography virtual colonoscopy, radiography with meglumine diatrizoate, endoscopic balloon dilatation, metallic stent implantation and later overall colonoscopy, coupled with the newfound knowledge of compound Qingdai pill-taking, led to a subsequent diagnosis of ischemic or toxic bowel disease with sigmoid colon stenosis. The patient was successfully treated by laparoscopic sigmoid colectomy, and postoperative pathological examination revealed ischemic or toxic injury of the sigmoid colon, providing a final diagnosis of drug-induced sigmoid colon stenosis. This case highlights that adequate awareness of drug-induced colon stenosis has a decisive role in avoiding misdiagnosis and mistreatment. The diagnostic and therapeutic experiences learnt from this case suggest that endoscopic balloon expansion and colonic metallic stent implantation as bridge treatments were demonstrated as crucial for the differential diagnosis of benign colonic stenosis. Skillful surgical technique and appropriate perioperative management helped to ensure the safety of our patient in subsequent surgery after long-term use of glucocorticoids.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".