Combined trabecular metal acetabular shell and augment for acetabular revision with substantial bone loss
Bibliographic record
Abstract
Trabecular metal (TM) augments are a relatively new option for reconstructing segmental bone loss during acetabular revision. We studied 34 failed hip replacements in 34 patients that were revised between October 2003 and March 2010 using a TM acetabular shell and one or two augments. The mean age of the patients at the time of surgery was 69.3 years (46 to 86) and the mean follow-up was 64.5 months (27 to 107). In all, 18 patients had a minor column defect, 14 had a major column defect, and two were associated with pelvic discontinuity. The hip centre of rotation was restored in 27 patients (79.4%). The Oxford hip score increased from a mean of 15.4 points (6 to 25) before revision to a mean of 37.7 (29 to 47) at the final follow-up. There were three aseptic loosenings of the construct, two of them in the patients with pelvic discontinuity. One septic loosening also occurred in a patient who had previously had an infected hip replacement. The augments remained stable in two of the failed hips. Whenever there was a loose acetabular component in contact with a stable augment, progressive metal debris shedding was evident on the serial radiographs. Complications included another deep infection treated without revision surgery. Good clinical and radiological results can be expected for bone-deficient acetabula treated by a TM cup and augment, but for pelvic discontinuities this might not be a reliable option.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".