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Record W4382049669 · doi:10.1111/pde.15383

Characterization of wound microbes in epidermolysis bullosa: A focus on <i>Pseudomonas aeruginosa</i>

2023· article· en· W4382049669 on OpenAlexaff
Margaret E. Scollan, Laura E. Levin, Anne W. Lucky, Kristen P. Hook, Kathleen Peoples, Anna L. Bruckner, James A. Feinstein, Elena Pope, Catherine McCuaïg, Julie Powell, Lawrence F. Eichenfield, Moise L. Levy, Lucia Z. Diaz, Sharon A. Glick, Amy S. Paller, John Browning, Kimberly D. Morel

Bibliographic record

VenuePediatric Dermatology · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicSkin and Cellular Biology Research
Canadian institutionsUniversité de MontréalCentre Hospitalier Universitaire Sainte-JustineHospital for Sick Children
FundersPediatric Dermatology Research AllianceNational Center for Advancing Translational SciencesNational Institutes of HealthIrving Medical Center, Columbia UniversityEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentGeorgia Clinical and Translational Science Alliance
KeywordsPseudomonas aeruginosaEpidermolysis bullosaMedicineStaphylococcus aureusWound careMicrobiologyPopulationDermatologyBacteriaIntensive care medicineBiologyEnvironmental health

Abstract

fetched live from OpenAlex

The most common bacteria isolated from wound cultures in patients recorded in the Epidermolysis Bullosa Clinical Characterization and Outcomes Database (EBCCOD) are Staphylococcus aureus and Pseudomonas aeruginosa. Given the prevalence of P. aeruginosa in this patient population and prior research implicating P. aeruginosa's potential role in carcinogenesis, we sought to further analyze patients with recorded wound cultures positive for Pseudomonas aeruginosa in the EBCCOD. We provide a descriptive analysis of this subset of patients and highlight potential avenues for future longitudinal studies that may have significant implications in our wound care management for patients with epidermolysis bullosa.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.010
GPT teacher head0.252
Teacher spread0.241 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2023
Admission routes1
Has abstractyes

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