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Record W4310368497 · doi:10.1177/11207000221139467

Is the routine use of local antibiotics in the management of periprosthetic joint infections justified?

2022· review· en· W4310368497 on OpenAlexaff
Jonathan Bourget-Murray, Marisa Ann Azad, Wade Gofton, Hesham Abdelbary, Simon Garceau, George Grammatopoulos

Bibliographic record

VenueHip International · 2022
Typereview
Languageen
FieldMedicine
TopicOrthopedic Infections and Treatments
Canadian institutionsOttawa Hospital
Fundersnot available
KeywordsPeriprostheticMedicineAntibioticsArthroplastyIntensive care medicineDebridement (dental)Joint arthroplastySurgery

Abstract

fetched live from OpenAlex

Periprosthetic joint infection (PJI) following total hip and total knee arthroplasty continues to be a leading cause of re-operation and revision arthroplasty. Not only is the treatment of PJI notoriously challenging, but success rates are variable. Regardless of the surgical strategy used, successful management of PJI requires a comprehensive surgical debridement focused at eradicating the underlying biofilm followed by appropriate antimicrobial therapy. Although systemic antimicrobial delivery continues to be a cornerstone in the treatment of PJI, many surgeons have started using local antibiotics to deliver higher concentrations of antibiotics directly into the vulnerable joint and adjacent soft tissues, which often have compromised vascularity. Available evidence on the use of topical powder, bone cement, and calcium sulphate carriers for local delivery of antibiotics during the initial treatment of PJI is limited to studies that are extremely heterogeneous. There is currently no level-1 evidence to support routinely using these products. Further, appropriately powered, prospective studies are needed to quantify the safety and efficacy of antibiotic-located calcium-sulphate carriers to justify their added costs. These products should not encourage surgeons to deviate from best practice guidelines, such as those recommended during the International Consensus Meeting on Musculoskeletal Infections.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.991
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.130
GPT teacher head0.366
Teacher spread0.236 · 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

Citations12
Published2022
Admission routes1
Has abstractyes

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