A Case of Fournier’s Gangrene in a Patient Taking Canagliflozin for the Treatment of Type II Diabetes Mellitus
Bibliographic record
Abstract
BACKGROUND Sodium-glucose cotransporter 2 (SGLT2) inhibitors are a class of antihyperglycemic medications associated with an increased risk of urinary and genital infections due to their glycosuric effects. In 2018, the FDA issued a safety alert warning that multiple cases of Fournier's Gangrene (FG), a severe genital infection, had been reported in patients taking SGLT2 inhibitors. CASE REPORT We present a case of 72-year-old male with type II diabetes mellitus who developed FG while taking the SGLT2 inhibitor canagliflozin. Besides diabetes and canagliflozin use, his other risk factors were his age, gender, and remote history of radiotherapy for prostate cancer. He presented to the Emergency Department (ED) multiple times complaining of rectal pain and was admitted for a possible diagnosis of prostatitis. During his stay, he developed leukocytosis, his pain worsened, and examination of the perianal area was consistent with FG. He was treated with multiple surgical debridement procedures and broad-spectrum antibiotics; the source of infection was determined to be a perianal abscess. He stayed in the hospital for 1 month and was discharged home with outpatient wound care and vacuum dressing changes. Canagliflozin was discontinued during the hospital stay. CONCLUSIONS Due to the possible association of FG with SGLT2 inhibitors, patients who present with signs and symptoms consistent with FG should be examined for possible FG and treated promptly.
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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) | 0.001 | 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 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".