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 distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".