The potential for 3D technology to be used in the first-stage periprosthetic hip joint infection treatment
Bibliographic record
Abstract
Abstract Background. In two-stage deep periprosthetic infection treatment, many authors describe mechanical complications associated with the implantation of a spacer in the first stage that affect the functional outcome of the treatment.Purpose. To evaluate the functional results of using 3D spacers for IIIA and IIIB defects according to the classification system described by W.G. Paprosky during the first stage treatment of hip deep periprosthetic joint infection (PJI).Methods. From 2017 to 2020, 24 patients underwent first-stage revision arthroplasty with hip PJI and IIIA and IIIB acetabular bone defects according to the classification system described by W.G. Paprosky. The patients were divided into 2 groups: group 1 received articulating spacers, and group 2 received custom-made spacers made using 3D technology. Function was evaluated by the Harris Hip Score, WOMAC (Western Ontario and McMaster Universities Osteoarthritis Index) and VAS (visual analogue scale). Statistical analyses were performed using IBM SPSS Statistics version 22.0 for Windows. Student's t-test, Wilcoxon's signed-rank test (to compare parameters before and after surgery) and the Mann-Whitney rank-sum test were used.Results. In the first group, the average VAS score was 3.3 (± 1.4), the Harris Hip Score was 51.3 (± 9.4), and the WOMAC score was 42.9 (± 5.9); in the second group, the VAS score was 1.3 (± 0.9), the Harris Hip Score was 69.7 (± 3.6), and the WOMAC score was 30.1 (± 2.4). The rating scale data showed a statistically significant improvement in the function of patients in the second group (p <0.05).Conclusion. Custom-made 3D spacers used during the first stage of treatment for deep periprosthetic hip infection yield larger improvements in function and quality of life than do articulating spacers.Trial registration. Local Ethics Committee at the Sechenov First Moscow State Medical University Ministry of Health of Russia (Sechenov University) № 386 13.12.2016.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".