Prosthetic joint infections: is guideline-consistent surgical treatment beneficial?
Bibliographic record
Abstract
Background: The diagnosis and treatment of prosthetic joint infection (PJI) remains challenging. In 2013, both the Infectious Diseases Society of America (IDSA) guidelines and an international consensus’ recommendation on PJI were published, providing a consistent approach to PJI management. We undertook a study to compare outcomes of PJI managed in accordance with IDSA versus those managed outside of the same. Methods: This retrospective cohort study of a consecutive series of patients who had total joint replacement (TJR) with subsequent deep PJI was undertaken to determine historical clinical variation relative to recently established management guidelines. All operations were completed at one arthroplasty center over a 5-year period predating IDSA guideline development. Results: Of 8505 patients who had TJR, 267 (3.1%) were diagnosed with subsequent PJI. Of these, 42/8505 (0.5%) had culture positive deep PJI, with 38/42 (90.5%) managed surgically. The odds of treatment failure among cases not managed in accordance with IDSA were 11 times greater as compared to guideline-accordant cases (OR 11, 95%CI 1.84-65.7; P=0.006). This difference was most pronounced among those who had irrigation and debridement. We could not demonstrate any significant difference in treatment success or failure for one-stage or two-stage exchange. Conclusions: Surgical management of PJI in accordance with existing guidelines can optimize success of PJI treatment. In particular, aggressive surgical treatment (including prosthesis removal) is likely warranted in patients who had symptoms of PJI for longer than 3 wk. In a patient in whom deviation from existing guidelines is considered, it is important for physicians to weigh the risk of inferior outcome and counsel the patient accordingly.
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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 | 0.004 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".