Povidone‐Iodine–Induced Burn: Case Report and Review of the Literature
Bibliographic record
Abstract
Burns are a rare but potentially serious complication of povidone-iodine use. This rare adverse drug reaction developed in a 38-year-old woman who underwent laparoscopic right ovarian cystectomy and endometrial ablation as a day procedure involving application of the topical antiseptic 10% povidone-iodine solution. Two days later, the patient was admitted to the hospital with burning, pain, itching, marked redness, and blistering extending from her midback to buttocks. A stain on her back also was evident. Partial-thickness chemical burn was diagnosed. Review of the literature yielded 13 other cases of povidone-iodine-induced burn. This underrecognized adverse effect of povidone-iodine application typically occurs when the povidone-iodine has not been allowed to dry or has been trapped under the body of a patient in a pooled dependent position. The burn is usually seen immediately after the procedure or on the next day, and typically heals with minimum scarring within 3-4 weeks with conservative treatment. The commonly postulated mechanism is a chemical burn due to irritation coupled with maceration, friction, and pressure. Given the widespread use of povidone-iodine and the potential for development of infection after a burn, clinicians need to be aware of this possible povidone-iodine-associated adverse drug reaction, and of preventive measures.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".