The effect of patient institutional transfer during the interstage period of two-stage treatment for prosthetic knee infection
Bibliographic record
Abstract
Aims The aim of this study was to assess the effects of transferring patients to a specialized arthroplasty centre between the first and second stages (interstage) of prosthetic joint infection (PJI) of the knee. Patients and Methods A search of our institutional database was performed to identify patients having undergone two-stage revision total knee arthroplasty (TKA) for PJI. Two cohorts were created: continuous care (CC) and transferred care (TC). Baseline characteristics and outcomes were collected and compared between cohorts. Results A total of 137 patients were identified: 105 in the CC cohort (56 men, 49 women; mean age 67.9) and 32 in the TC cohort (17 men, 15 women; mean age 67.8 years). PJI organism virulence was greater in the CC cohort (36.2% vs 15.6%; p = 0.030). TC patients had a higher rate of persisting or recurrent infection (53.6% vs 13.4%; p < 0.001), soft-tissue complications (31.3 vs 14.3%; p = 0.030), and reduced requirement for porous metal augments (78.1% vs 94.3%; p = 0.006). Repeat first stage debridement after transfer led to greater need for plastic surgical procedures (58.3% vs 0.0%; p < 0.001). Conclusion Patient transfer during the interstage of treatment for infected TKA leads to poorer outcomes compared with patients receiving all their treatment at a specialized arthroplasty centre. Cite this article: Bone Joint J 2019;101-B:1087–1092.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".