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Periprosthetic joint infection

2025· article· en· W4409015332 on OpenAlexaff
John W. Kennedy, Fares S. Haddad

Bibliographic record

VenueThe Bone & Joint Journal · 2025
Typearticle
Languageen
FieldMedicine
TopicOrthopedic Infections and Treatments
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsPeriprostheticDelphi methodMedicineComparabilityDelphiIntensive care medicineArthroplastyFamily medicineSurgeryComputer science

Abstract

fetched live from OpenAlex

Aims: Periprosthetic joint infection (PJI) is a devastating complication of arthroplasty, with substantial morbidity, mortality, and healthcare costs. Despite advances in diagnosis and treatment, inconsistencies in outcome reporting have hindered evidence synthesis, limiting progress in understanding and management. This study aimed to develop a core outcome set (COS) for PJI to standardize outcome reporting in the literature. Methods: A two-stage modified Delphi process was conducted to establish consensus across a range of domains. Stage 1 involved the identification of core outcomes in PJI research by an international expert panel. A patient group was also consulted to ensure that the domains were relevant to patient priorities. Stage 2 included a broader group of 55 stakeholders in an online consensus process to finalize the COS. Quantitative and qualitative data were collated to redefine the outcomes throughout the Delphi process. Results: Following the modified two-stage Delphi process, a high level of consensus was achieved for all outcomes. The final COS included 23 outcomes across the following four domains: patient demographics and baseline characteristics; infection characteristics; surgical and treatment details; and outcomes and follow-up. Conclusion: The developed COS provides a standardized framework for reporting outcomes in PJI research. By addressing variability and inconsistency in the literature, this COS aims to enhance comparability across studies, support robust evidence synthesis, and ultimately guide clinical decision-making.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.582
Threshold uncertainty score0.537

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.020
GPT teacher head0.280
Teacher spread0.260 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations6
Published2025
Admission routes1
Has abstractyes

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