Retrieval analysis of a failed TriboFit polycarbonate urethane acetabular buffer
Bibliographic record
Abstract
The purpose of this research was to determine the failure mechanisms and damage features of a TriboFit acetabular buffer implanted directly against a native, prepared acetabulum which was revised after 11months. Retrieval analyses were carried out via light microscopy, gravimetric wear assessment, and observer scoring of visible damage features on the buffer. The volume of material abraded from the backside of the buffer was estimated via three-dimensional reconstruction using a laser scanner. Scanning electron microscopy was used to confirm damage features and mechanisms. Severe abrasion to the backside of the buffer was the primary damage feature, while stippling damage was seen on the articular surface of the buffer. Material loss due to backside abrasion was approximated to be between 0.13360.085 g (gravimetric analyses) and 0.19360.053 g (three-dimensional reconstruction). Implantation of the TriboFit buffer against the patient's native acetabulum without a metal backing allowed for significant movement of the buffer against the bone, resulting in the abrasion seen on this implant. The stippling damage on the articular surface indicates an adhesive wear mechanism which exacerbates movement of the buffer against the acetabulum, thereby increasing backside abrasion.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.001 | 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".