Retrieval analysis and <i>in vitro</i> assessment of strength, durability, and distraction of a modular total hip replacement
Bibliographic record
Abstract
We investigated a commercial Co-Cr-alloy head--Ti6Al4V alloy neck and Ti6Al4V stem modular total hip replacement. We assessed the distraction forces after in vitro cycling in bovine serum, fatigue durability, fretting corrosion damage, and load bearing capacity of new implants using fatigue-corrosion, pull-off, scanning electron microscopy, fatigue and compression investigations. In addition, we studied corrosion, fretting damage, and distraction forces on retrievals. For both retrievals and in vitro test samples, the neck-stem interface required the higher distraction force as compared with the head-neck interface. One of 12 retrievals showed strong fretting corrosion at the neck-stem interface which resulted in a high disassembly force of about 16 kN. For in vitro test samples, the neck-stem pull-off force initially increased during cycling and showed a maximum value of 5.704 kN at ∼100,000 cycles, which is equivalent to gait cycles performed in approximately 36 days. Overall, assembly force, initial component settling, and interface corrosion primarily determine the force required to distract the modular components. One million cycles fatigue failure of the neck can be expected at a maximum compression load of -6.5 kN. No component failure was observed during quasistatic compression; rather the neck deformed plastically and the ultimate compression load-bearing capacity was -13 kN.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".