Management of tattoo-associated <i>Mycobacterium abscessus</i> skin infections by surgery and observation
Bibliographic record
Abstract
Mycobacterium abscessus, a rapidly growing and frequently multidrug-resistant non-tuberculous mycobacterium, has been linked to cutaneous infections in both immunocompetent and immunocompromised individuals through inoculation via skin-traumatizing procedures such as tattooing. Here we describe two patients with culture-confirmed M. abscessus cutaneous infections acquired from the same tattoo studio in Calgary, Canada. Both patients developed dozens of erythematous papules at the site of inoculation within 2 weeks of a new tattoo using the same grey-wash ink. Despite recommendation for antimicrobial therapy, both patients independently elected to pursue surgical excision and observation. Following excision of multiple papules, both patients showed evidence of infection resolution within 10 weeks, without any evidence of relapsed infection. Conventional treatment of M. abscessus skin and soft tissue infections typically involves susceptibility-guided multidrug regimens for several months and is commonly associated with significant medication toxicity. These cases demonstrate that observation, potentially augmented with surgical removal, may be effective alternatives, especially in immunocompetent individuals with localized infections, which avoids the need for antibiotics.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".