Antibiotic‐resistant <i>Propionibacterium acnes</i> among acne patients in a regional skin centre in Hong Kong
Bibliographic record
Abstract
BACKGROUND: There has been no study on antibiotic-resistant Propionibacterium acnes in Hong Kong. OBJECTIVE: We investigated the prevalence and pattern of antibiotic-resistant P. acnes and to identify any associated factors for harbouring the resistant strains. METHODS: Culture and sensitivity testing of P. acnes to commonly used antibiotics were performed. Resistance to tetracycline was defined at a minimal inhibitory concentration (MIC) of 2 μg/mL or more; erythromycin at an MIC of 0.5 μg/mL or more; clindamycin at an MIC of 0.25 μg/mL or more according to EUCAST. For breakpoints of doxycycline and minocycline, those with an MIC of 1 μg/mL or more were defined as resistant strains. RESULTS: Among the 111 specimens collected from 111 patients, 86 strains of P. acnes were recovered, one from each specimen. Twenty-five specimens had no growth. Forty-seven (54.8%) strains were found to be resistant to one or more antibiotics. Forty-six (53.5%), 18 (20.9%), 14 (16.3%), 14(16.3%) and 14 (16.3%) strains were resistant to clindamycin (CL), erythromycin (EM), tetracycline (TET), doxycycline (DOX) and minocycline (MR) respectively. Ten strains (11.6%) had cross resistance between the MLS antibiotics (erythromycin or clindamycin), one strain (1.2%) had cross resistance among the cyclines and 14 strains (16.4%) had cross resistance between the MLS and cycline antibiotics. Binary logistic regression showed an association between MLS antibiotic resistance with an increased age whereas cycline resistance was associated with the duration of treatment. CONCLUSION: Antibiotic-resistant P. acnes is prevalent in Hong Kong. Dermatologists should be more vigilant in prescribing antibiotics for acne patients.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".