The case of a bloody mess – Bictegravir/emtricitabine/tenofovir alafenamide induced colitis
Bibliographic record
Abstract
• Gross elevation of fecal calprotectin can be indicative of pancolitis. • Drugs, bloody diarrhea, other pre-analytical factors increase fecal calprotectin. • Biktarvy may increase fecal calprotectin, secondary to drug induced colitis. Fecal calprotectin is a marker used to differentiate inflammatory bowel disease versus irritable bowel syndrome and is relevant in the diagnosis of ulcerative colitis and Crohn’s disease. Markedly elevated calprotectin from stool samples provides evidence of colonic inflammation to support the diagnosis of pancolitis. This report is the first to demonstrate the clinical significance of fecal calprotectin in supporting the diagnosis of pancolitis induced by the anti-viral drug, Biktarvy (bictegravir/emtricitabine/tenofovir alafenamide). A 62-year-old male on Biktarvy for his HIV diagnosis was admitted to internal medicine with abdominal pain, bloody diarrhea and pancolitis. His white blood cell count was 15.8 (4.0–11.0x10 9 /L), neutrophil count was 9.7 (2.0–7.5 × 10 9 /L), monocyte count was 1.2 (0.2–0.8 × 10 9 /L), granulocyte count was 1.5 (≤0.1 × 10 9 /L) and hemoglobin was 163 (140–180 g/L). The patient had a C-reactive protein of 229 (≤11.0 mg/L). Serology and blood culture were negative for microbial testing and abdomino-pelvic computed tomography findings were unremarkable. A bloody stool collected had a fecal calprotectin level of 1,159 (<50 µg/g). This case highlights how anti-retroviral therapies such as Biktarvy may elicit medication-induced gastrointestinal symptoms, which may underlie the cause of bloody diarrhea and pancolitis, and consequently a grossly elevated fecal calprotectin.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.007 | 0.006 |
| 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".