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Record W2960618850 · doi:10.1002/hed.25856

Systematic review of international guidelines for perioperative antibiotic prophylaxis in Head & Neck Surgery. A YO‐IFOS Head & Neck Study Group Position Paper

2019· review· en· W2960618850 on OpenAlexaff
Carlos M. Chiesa‐Estomba, Jérôme R. Lechien, Nicolas Fakhry, Antoine E. Melkane, Christian Calvo‐Henríquez, Daniele de Siati, José Ángel González-García, Johannes J. Fagan, Tareck Ayad

Bibliographic record

VenueHead & Neck · 2019
Typereview
Languageen
FieldMedicine
TopicSurgical site infection prevention
Canadian institutionsCentre Hospitalier de l’Université de Montréal
FundersTata Memorial CentreChinese University of Hong KongUniversity of Hong Kong
KeywordsMedicineAntibiotic prophylaxisPerioperativeAntibioticsHead and neckSurgeryIncidence (geometry)Head and neck cancerAntibiotic resistanceAntimicrobialGeneral surgeryRadiation therapy

Abstract

fetched live from OpenAlex

BACKGROUND: Surgical site infection (SSI) is defined as an infection that occurs after a surgical incision or organ manipulation during surgery. The frequency reported for clean head and neck surgical procedures without antimicrobial prophylaxis is <1%. In contrast, infection rates in patients undergoing complicated cancer surgery are high, ranging from 24% to 87% of patients without antimicrobial prophylaxis. METHODS: Guidelines and recommendations about the use of antibiotics in head and neck surgery from 2004 to 2019 were reviewed. RESULTS: Four guidelines from Oceania, 5 from South America, 5 from North America, 2 from the United Kingdom, 11 from Europe, 1 from Africa, 1 from the Middle East, and 3 from Asia were included. A total of 118 papers were included for analysis and recommendation. CONCLUSION: Antibiotic prophylaxis can decrease the incidence of SSI. However, the risks associated with antibiotic exposure and the risk of antibiotic resistance need to be taken into consideration.

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)0.003
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.296
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0070.002
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.147
GPT teacher head0.442
Teacher spread0.295 · 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 designSystematic review
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

Citations37
Published2019
Admission routes1
Has abstractyes

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