<i>Mycobacterium haemophilum</i> infection with cutaneous involvement: two case reports and an updated literature review
Bibliographic record
Abstract
Mycobacterium haemophilum (MH) is a slow-growing, non-tuberculous Mycobacterium that most commonly causes infections in immunocompromised patients. The skin is the most prevalent site of infection and can be an isolated presentation or part of a disseminated disease. Herein, we reported a case of isolated MH infection of the hand and a case of disseminated MH infection with multiple skin lesions. In addition, other MH cases with cutaneous involvement over the last 10 years, from 2011-2022, were reviewed and analyzed. Among the 79 included cases, the common skin findings in MH infections included nodules, ulcers, abscesses, swelling, and pustules. Middle-aged patients with iatrogenic immunosuppression from glucocorticoids, mycophenolate mofetil, cyclosporine, and cyclophosphamide are the most susceptible to MH infection, with a higher risk of dissemination to internal organs. Disseminated MH infections commonly present as tenosynovitis, arthritis/arthralgia, or osteomyelitis. There is a lack of strong evidence for treatment; however, triple therapy of quinolone, macrolides, and rifampicin is most often used in clinical practice. The overall prognosis is good. The presence of iatrogenic immunocompromised diseases, lesions involving the proximal limbs, and dissemination of MH infections are associated with worse clinical outcomes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".