Cellulitis Secondary to Liquid Nitrogen Cryotherapy: Case Report and Literature Review
Bibliographic record
Abstract
BACKGROUND: Liquid nitrogen cryotherapy is a commonly used technique to treat a wide variety of dermatologic conditions including actinic keratoses, non-melanoma skin cancers, verrucae, and seborrheic keratoses. The risks associated with liquid nitrogen cryotherapy are important to know and discuss with patients prior to treatment. OBJECTIVE: We report a case of cellulitis secondary to liquid nitrogen cryotherapy for actinic keratosis. We sought to review the literature for an estimate of secondary infection rates following cryotherapy treatment. METHODS: We searched Pubmed using the terms cryotherapy and infection or cellulitis. We then looked at articles classified as clinical trials where cryotherapy was used to treat skin conditions. We then selected clinical trials that listed cellulitis or infection as an adverse event. RESULTS AND CONCLUSION: There were no case reports, case series, or review articles detailing the risk of infection from liquid nitrogen cryotherapy. We found 8 articles classified as clinical trials on Pubmed that did list infection as an adverse event. The risk of infection from these studies varied from approximately 2% to 30%. There was a great degree of heterogeneity in treatment sites, length of treatment, and treatment targets. While it is difficult to determine the true incidence of infection from liquid nitrogen cryotherapy, clinicians should endeavor to inform patients of this potential risk.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".