Characterization of wound microbes in epidermolysis bullosa: Results from the epidermolysis bullosa clinical characterization and outcomes database
Bibliographic record
Abstract
BACKGROUND/OBJECTIVES: Patients with epidermolysis bullosa (EB) require care of wounds that are colonized or infected with bacteria. A subset of EB patients are at risk for squamous cell carcinoma, and bacterial-host interactions have been considered in this risk. The EB Clinical Characterization and Outcomes Database serves as a repository of information from EB patients at multiple centers in the United States and Canada. Access to this resource enabled broad-scale analysis of wound cultures. METHODS: A retrospective analysis of 739 wound cultures from 158 patients from 13 centers between 2001 and 2018. RESULTS: Of 152 patients with a positive culture, Staphylococcus aureus (SA) was recovered from 131 patients (86%), Pseudomonas aeruginosa (PA) from 56 (37%), and Streptococcus pyogenes (GAS) from 34 (22%). Sixty-eight percent of patients had cultures positive for methicillin-sensitive SA, and 47%, methicillin-resistant SA (18 patients had cultures that grew both methicillin-susceptible and methicillin-resistant SA at different points in time). Of 15 patients with SA-positive cultures with recorded mupirocin susceptibility testing, 11 had mupirocin-susceptible SA and 6 patients mupirocin-resistant SA (2 patients grew both mupirocin-susceptible and mupirocin-resistant SA). SCC was reported in 23 patients in the entire database, of whom 10 had documented wound cultures positive for SA, PA, and Proteus species in 90%, 50%, and 20% of cases, respectively. CONCLUSIONS: SA and PA were the most commonly isolated bacteria from wounds. Methicillin resistance and mupirocin resistance were reported in 47% and 40% of patients tested, respectively, highlighting the importance of ongoing antimicrobial strategies to limit antibiotic resistance.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".