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Record W4375845576 · doi:10.1093/ofid/ofad246

The Impact of Antimicrobial Therapy Duration in the Treatment of Prosthetic Joint Infections Depending on Surgical Strategies: A Systematic Review and Meta-analysis

2023· review· en· W4375845576 on OpenAlexaff
Flaminia Olearo, Veronica Zanichelli, Aimilia Exarchakou, Anna Both, Ilker Uςkay, Martin Aepfelbacher, Holger Rohde

Bibliographic record

VenueOpen Forum Infectious Diseases · 2023
Typereview
Languageen
FieldMedicine
TopicOrthopedic Infections and Treatments
Canadian institutionsOttawa Hospital
Fundersnot available
KeywordsMedicineMeta-analysisOdds ratioConfidence intervalMEDLINEAntibioticsAntimicrobialWeb of scienceInternal medicineSurgery

Abstract

fetched live from OpenAlex

The aim of this systematic review was to address the question if short antibiotic treatment (SAT; at least 4 but <12 weeks) versus long antibiotic treatment (LAT) affects outcomes in prosthetic joint infections (PJIs). Database research (Medline, Embase, Web of Science, Scopus, Cochrane) retrieved 3740 articles, of which 10 studies were included in the analysis. Compared to LAT, 11% lower odds of treatment failure in the SAT group were found, although the difference was not statistically significant (pooled odds ratio, 0.89 [95% confidence interval, .53-1.50]). No difference in treatment failure was found between SAT and LAT once stratified by type of surgery, studies conducted in the United States versus Europe, study design, and follow-up. There is still no conclusive evidence that antibiotic treatment of PJIs for 12 weeks or longer is associated with better outcomes, irrespective of the type of surgical procedure. Most recent, high-quality studies tend to favor longer antibiotic courses, making them preferable in most situations.

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)0.012
metaresearch head score (Gemma)0.028
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: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.020
Threshold uncertainty score0.063

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.028
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0200.033
Bibliometrics0.0070.008
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0020.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.103
GPT teacher head0.423
Teacher spread0.321 · 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

Citations15
Published2023
Admission routes1
Has abstractyes

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