CLINICAL OUTCOME AFTER INFECTED TOTAL KNEE AND TOTAL HIP ARTHROPLASTY
Bibliographic record
Abstract
OBJECTIVE: Infection after total hip (THA) and knee arthroplasty (TKA) is a serious complication which typically leads to a long lasting and intensive surgical and medicamentous treatment. The aim of this study was to identify factors that influence outcome after revision surgery caused by prosthetic infection. METHODS: We retrospectively analyzed 64 patients who had revision surgery between 1989 and 2009 due to periprosthetic infection. We examined a total of 69 joints (TKA: 36%, THA: 64%), follow-up 5.1 years (0.5-21 years) after the initial surgical intervention. The mean patient age at time of surgery was 67 years old (43-79 years old). Clinical data and scores including the Western Ontario and McMaster Universities (WOMAC)-Index, the Harris Hip Score (HHS) and the Hospital for Special Surgery Score (HSS) were surveyed. RESULTS: There was no difference in clinical scores regarding treatment between a single and a multiple stage treatment regime. Infections with multiple microorganisms and Enterococcus spp. lead to a significantly higher number of interventions. Using a modified Tsukayama system we classified 24% as type I, 34% type II and 42% type III- infections, with no differences in clinical outcome. Overweight patients had a significantly lower HHS and WOMAC-score. Immunosuppression leads to a worse WOMAC and HSS-Score. An increased number of procedures was associated to a limping gait. CONCLUSION: Thorough surgical technique leads to good clinical results independent of infection-type and treatment philosophy. Level of Evidence III, Case Control Study.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".