Bibliographic record
Abstract
Segmental defects of the acetabulum are often encountered in revision surgery. Many times these can be handled with hemispherical cups. However when larger defects are encountered particularly involving the dome and/or posterior wall structural support for the cup is often needed. In the past structural allograft was used but for the last 12 years at our institution trabecular metal augments have been used in the place of structural allograft in all cases. This talk will focus on technique and mid-term results using augments in association with an uncemented revision shell. The technique can be broken down into 6 steps outlined below: 1. Exposure, 2. Reaming, 3. Trialing, 4. Augment Inserted, 5. Cup Insertion/Stabilization, 6. Trial Reduction/Liner Cementation A recent study was undertaken to assess the mid-term results of this technique. We prospectively followed the first 56 patients in whom these augments were utilised in combination with a trabecular metal acetabular component in our unit. Details of this study will be presented. The median follow up of the surviving patients was 110 months (range 88–128 months). Survivorship of the augments at 10 years was 92.2% (95% CI: 97.0–80.5%). In one case the augment was revised for infection and in 3 for loosening. In 1 of the revised cases there was a pre-operative pelvic discontinuity, the other 2 discontinuities in the series were not revised and remain asymptomatic. Conclusions The results of the acetabular trabecular metal augments continue to be encouraging in the medium to long term with low rates of revision or loosening in this complex group of patients.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".