Functional outcome of infected endoprosthesis: A 20-year retrospective analysis
Bibliographic record
Abstract
PURPOSE: Bone tumours are increasingly treated with limb-salvage surgeries. However, implant infection is a devastating complication, greatly affecting the functional outcome. Yet, data on functional outcome post-implant infection are scarce. This study aims to determine the functional outcome and implant survival of these patients. METHODS: Patients' data on endoprosthetic replacement surgeries at our institution (January 1996-December 2016) was retrospectively reviewed. Information was available for 161 patients and was analysed using SPSS and SMART Partial Least Squares. Functional outcome was determined using the Musculoskeletal Tumor Society (MSTS) and Toronto Extremity Salvage Score (TESS) scoring system. RESULTS: Both mean rank MSTS (33.14 vs 87.02) and TESS (48.17 vs 85.13) scores were significantly lower in the infected group. These differences remained statistically significant after excluding amputation and rotationplasty cases within the infected group. Even after the resolution of infection, both MSTS and TESS remained significantly higher in the non-infected group. However, analysis of the infected group showed no significant differences in functional outcome between persistent and resolved infections (implant in-situ). Age significantly impacted the functional outcome for both the non-infected and infected groups, while local recurrence and metastasis significantly impacted the non-infected cases. Local tumour recurrence was lower in infected endoprosthetic patients (8.3% vs 10.5%). 56% of infected implants were removed; the majority were treated with two-stage revision surgery. CONCLUSION: Endoprosthesis infection worsens the overall functional outcome. Additional factors affecting functional outcome were age, presence of local recurrence and metastatic disease. Local tumour recurrence was lower amongst infected endoprosthesis cases, and >50% of infected implants were removed.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".