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Record W2790783712 · doi:10.1136/bmjopen-2017-020991

Antibiotics for uncomplicated skin abscesses: systematic review and network meta-analysis

2018· review· en· W2790783712 on OpenAlexaff
Wen Wang, Wenwen Chen, Yanmei Liu, Reed Siemieniuk, Ling Li, Juan Pablo Díaz Martinez, Gordon Guyatt, Xin Sun

Bibliographic record

VenueBMJ Open · 2018
Typereview
Languageen
FieldMedicine
TopicInfectious Diseases and Tuberculosis
Canadian institutionsImpactMcMaster UniversityUniversity of Toronto
FundersNational Natural Science Foundation of China
KeywordsMedicineAntibioticsMeta-analysisInternal medicineSubgroup analysisMEDLINEGuidelineRandomized controlled trialRelative riskTrimethoprimPublication biasCochrane LibraryIntensive care medicineSurgeryConfidence interval

Abstract

fetched live from OpenAlex

OBJECTIVE: To assess the impact of adjunctive antibiotic therapy on uncomplicated skin abscesses. DESIGN: Systematic review and network meta-analysis. DATA SOURCES: Medline, Embase, the Cochrane Central Register of Controlled Trials and ClinicalTrials.gov. STUDY SELECTION: A BMJ Rapid Recommendation panel provided input on design, important outcomes and the interpretation of the results. Eligible randomised controlled trials (RCTs) included a comparison of antibiotics against no antibiotics or a comparison of different antibiotics in patients with uncomplicated skin abscesses, and reported outcomes prespecified by the linked guideline panel. REVIEW METHODS: Reviewers independently screened abstracts and full texts for eligibility, assessed risk of bias and extracted data. We performed random-effects meta-analyses that compared antibiotics with no antibiotics, along with a limited number of prespecified subgroup hypotheses. We also performed network meta-analysis with a Bayesian framework to compare effects of different antibiotics. Quality of evidence was assessed with The Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach. RESULTS: Fourteen RCTs including 4198 patients proved eligible. Compared with no antibiotics, antibiotics probably lower the risk of treatment failure (OR 0.58, 95% CI 0.37 to 0.90; low quality), recurrence within 1 month (OR 0.48, 95% CI 0.30 to 0.77; moderate quality), hospitalisation (OR 0.55, 95% CI 0.32 to 0.94; moderate quality) and late recurrence (OR 0.64, 95% CI 0.48 to 0.85; moderate quality). However, relative to no use, antibiotics probably increase the risk of gastrointestinal side effects (trimethoprim and sulfamethoxazole (TMP-SMX): OR 1.28, 95% CI 1.04 to 1.58; moderate quality; clindamycin: OR 2.29, 95% CI 1.35 to 3.88; high quality) and diarrhoea (clindamycin: OR 2.71, 95% CI 1.50 to 4.89; high quality). Cephalosporins did not reduce the risk of treatment failure compared with placebo (moderate quality). CONCLUSIONS: In patients with uncomplicated skin abscesses, moderate-to-high quality evidence suggests TMP-SMX or clindamycin confer a modest benefit for several important outcomes, but this is offset by a similar risk of adverse effects. Clindamycin has a substantially higher risk of diarrhoea than TMP-SMX. Cephalosporins are probably not effective.

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)0.031
metaresearch head score (Gemma)0.078
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.031
Threshold uncertainty score0.164

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0310.078
Meta-epidemiology (narrow)0.0040.002
Meta-epidemiology (broad)0.0230.041
Bibliometrics0.0100.009
Science and technology studies0.0010.001
Scholarly communication0.0040.003
Open science0.0030.002
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.277
GPT teacher head0.498
Teacher spread0.220 · 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 designMeta-analysis
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

Citations43
Published2018
Admission routes1
Has abstractyes

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