Short-term Results of a Custom Triflange Acetabular Component for Massive Acetabular Bone Loss in Revision THA
Bibliographic record
Abstract
Managing severe periacetabular bone loss during revision total hip arthroplasty (THA) is a challenging task. Multiple treatment options have been described. A custom triflanged acetabular component is a recent treatment option. The authors retrospectively reviewed 19 hips in 19 patients with massive periacetabular bone loss (Paprosky types 3A/3B and AAOS types III/IV) treated with custom triflanged acetabular components. Mean patient age at surgery was 58 years (range, 42-79 years).At an average follow-up of 31 months (range, 16-59 months), mean Harris Hip Score had improved from 38 preoperatively to 63 postoperatively, and mean Western Ontario McMaster Osteoarthritis Index scores had improved from 43 preoperatively to 26 postoperatively. Sixty-five percent of cases were considered successful. Three (16%) patients had significant complications; 2 (11%) custom triflanged acetabular components were removed due to failure. At last follow-up, 6 (43%) of 14 patients reported that their ambulatory status was improved vs their preoperative status, 3 (21%) reported no change, and 5 (36%) reported that their ambulatory status was worse than their preoperative status.In this study, the use of a custom triflanged acetabular component for massive periacetabular bone loss in revision THA had less favorable results than in other reports. Use of a custom triflanged acetabular component for massive periacetabular bone loss in revision THA remains a viable option, but surgeon and patient expectations should be realistic.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".