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Record W2158640421 · doi:10.1111/ijd.13010

New and emerging concepts in managing and preventing community‐associated methicillin‐resistant <i>Staphylococcus aureus</i> infections

2015· review· en· W2158640421 on OpenAlexaff
Aditya K. Gupta, Danika C.A. Lyons, Ted Rosén

Bibliographic record

VenueInternational Journal of Dermatology · 2015
Typereview
Languageen
FieldMedicine
TopicAntimicrobial Resistance in Staphylococcus
Canadian institutionsMediprobe Research (Canada)University of Toronto
Fundersnot available
KeywordsMedicineStaphylococcus aureusHygieneMethicillin-resistant Staphylococcus aureusTransmission (telecommunications)Intensive care medicineStaphylococcal Skin InfectionsSkin infectionIntervention (counseling)Infection controlStaphylococcal infectionsDermatologyBacteriaPathologyNursing

Abstract

fetched live from OpenAlex

Methicillin-resistant Staphylococcus aureus (MRSA) infections occurring within communities are increasing and can affect healthy individuals who have had little to no experience with hospital or healthcare settings (community-associated MRSA, CA-MRSA). CA-MRSA infections have multiple presentations which can make diagnosis and timely treatment difficult yet often manifest as a skin and soft tissue infection (SSTI) requiring dermatological intervention. There is emerging evidence of multiple environmental sources of bacteria that may contribute to recurrence. As with other infections, preventing transmission and recurrence depends on adherence to hand-washing and personal hygiene practices. Pharmaceutical intervention should be culture- rather than empirically-guided. The goal of this review is to provide dermatologists with a brief summary of the diagnostic features of CA-MRSA infections and updated strategies for management and prevention of transmission and recurrence of CA-MRSA infections, infections likely to present to dermatology offices.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.945
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0000.000

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 head0.043
GPT teacher head0.389
Teacher spread0.346 · 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 teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreReview

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

Citations19
Published2015
Admission routes1
Has abstractyes

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