Bibliographic record
Abstract
The indications for cementless acetabular fixation have been broadened because our data supports the use of trabecular metal cups even when there9s limited bleeding host bone contact. Trabecular metal augments have allowed us to use cementless cups when there is segmental loss of bone. Surgical Technique: The acetabular bed is prepared. If there is less medial bone stock than 2mm, then morselised allograft is impacted by reverse reaming. When reaming is complete and less than 50% bleeding host bone is available for cup stabilization, then a trabecular metal cup is indicated. Trabecular augments are used if the trabecular cup trial is not stable, or if it is uncovered by 40% or more. The conventional augments come in different sizes to accommodate the diameter of the cup and the size of the defect. Larger defects are addressed with anterior and posterior column augments, and superior defects with figure of seven augments. Augments are fixed with at least two screws. The interface between the cup and the augments should be stable, but some surgeons place a very thin layer of cement between the augment and cup so micromotion does not occur while ingrowth is occurring. We have used trabecular metal augments in 46 acetabular revisions in conjunction with a TM cup. Thirty-four cases have at least 2 years follow-up with an average of 64.5 months. There has been 4 cup loosenings with 3 re-revisions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".