Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".