Low incidence of recurrent Buruli ulcers in treated Australian patients living in an endemic region
Bibliographic record
Abstract
We examined recurrent Buruli ulcer cases following treatment and assumed cure in a large cohort of Australian patients living in an endemic area.We report that while the recurrence rate was low (2.81 cases/year/1000 population), it remained similar to the estimated risk of primary infection within the general population of the endemic area (0.85-4.04 cases/year/ 1,000 population).The majority of recurrent lesions occurred in different regions of the body and were separated by a median time interval of 44 months.Clinical, treatment and epidemiological factors combined with whole genome sequencing of primary and recurrent isolates suggests that in most recurrent cases a re-infection was more likely as opposed to a relapse of the initial infection.Additionally, all cases occurring more than 12 months after commencement of treatment were likely re-infections.Our study provides important prognostic information for patients and their health care providers concerning the nature and risks associated with recurrent cases of Buruli ulcer in Australia. Author summaryMycobacterium ulcerans (M.ulcerans) causes a necrotising infection of skin and soft-tissue known as Buruli ulcer.Since the regular use of antibiotics for Buruli ulcer treatment in Australian populations was introduced at the turn of the century, treatment success rates have been very high.However there is no information from the Australian setting on the risk of recurrent disease following treatment and assumed cure, despite this being important prognostic information for patients, their families and health-care providers.Furthermore, it is also not known if recurrent disease represents late relapse of the initial treated infection or a subsequent new infection.In our study we have shown for the first time in Australian patients living in an endemic area that the incidence of recurrent Buruli ulcer following treatment and healing is low, and that this risk is similar to the estimated risk of
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".