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Record W2344766653 · doi:10.1089/sur.2013.134

Infection in Burns

2016· review· en· W2344766653 on OpenAlexfundno aff
William B. Norbury, David N. Herndon, Jessica Tanksley, Marc G. Jeschke, Celeste C. Finnerty

Bibliographic record

VenueSurgical Infections · 2016
Typereview
Languageen
FieldMedicine
TopicBurn Injury Management and Outcomes
Canadian institutionsnot available
FundersNational Center for Advancing Translational SciencesNational Center for Research ResourcesNational Institute of General Medical SciencesCanadian Institutes of Health Research
KeywordsMedicineAntimicrobialInfection controlIntensive care medicineCandida albicansAntibioticsVancomycinStaphylococcus aureusEnterococcusSurgeryMicrobiologyBacteria

Abstract

fetched live from OpenAlex

BACKGROUND: Developments in critical care and surgical approaches to treating burn wounds, together with newer antimicrobial treatments, have significantly reduced the morbidity and mortality rates associated with this injury. METHODS: Review of the pertinent English-language literature. RESULTS: Several resistant organisms have emerged as the maleficent cause of invasive infection in burn patients, including methicillin-resistant Staphylococcus aureus, vancomycin-resistant Enterococcus, Pseudomonas, Acinetobacter, non-albicans Candida spp., and Aspergillus. Advances in antimicrobial therapies and the release of new classes of antibiotics have certainly added to the armamentarium of therapeutic resources for the clinician. CONCLUSION: Strict infection control measures, constant wound surveillance with regular sampling of tissues for quantitative culture, and early excision and wound closure remain the principal adjuncts to control of invasive infections in burn patients.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.005
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0050.002

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 head0.062
GPT teacher head0.394
Teacher spread0.332 · 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 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

Citations323
Published2016
Admission routes1
Has abstractyes

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