Fellowship Training in Arthroplasty Improves Treatment Success of Debridement, Antibiotics, and Implant Retention for Periprosthetic Knee Infections
Bibliographic record
Abstract
Background Debridement, antibiotics, and implant retention (DAIR) is a well-accepted surgical strategy for periprosthetic joint infection (PJI) following total knee arthroplasty (TKA). DAIR in TKA may be incorrectly thought of as a "simple" procedure not requiring formal specialized training in arthroplasty. Currently, there are no studies comparing the risk of treatment failure based on surgeon fellowship training. Methods A retrospective review was performed of consecutive patients who underwent DAIR for TKA PJI at our institution. Two cohorts were created based on whether DAIR was performed by an arthroplasty fellowship-trained (FT) surgeon or nonarthroplasty fellowship-trained (NoFT) surgeon. Primary outcome was treatment failure following DAIR at a minimum of 1 year postoperatively. Treatment failure was based on the Tier 1 International Consensus Meeting definition of infection control. Secondary outcomes were also recorded including death during the totality of PJI treatment. Results A total of 112 patients were identified (FT = 68, NoFT = 44). At a mean follow-up of 7.3 years [standard deviation = 3.9], 73 patients (59.8%) failed treatment. Fellowship training in arthroplasty significantly improved treatment success rates (FT, 35/68 [51.5%]; NoFT, 10/44 [22.7%]; odds ratio 2.5 [95% confidence interval 1.1 to 5.9; P = .002]). Survivorship also differed significantly between the cohorts; at timepoints of 1.5 months, 5 months, 30 months, and 180 months, survivorship of the FT cohort was 79.4%, 67.6%, 54.4%, and 50.7%, respectively, compared with a survivorship of 65.9%, 52.3%, 25%, and 22.7% in the NoFT cohort ( P = .002). Conclusions TKA PJI treated with DAIR should not be considered a simple procedure. Improved treatment success may be associated with subspecialty fellowship training in arthroplasty. Level of Evidence IV.
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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 | 0.002 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".