The effect of altering head length on corrosion using a material loss method
Bibliographic record
Abstract
Introduction: Corrosion at head neck taper junctions in total hip arthroplasty has increasingly been reported in the literature. Debate persists as to the exact causes and clinical significance of corrosion. Increased offset and head length has been correlated with an increased risk of tribocorrosion due to an adverse mechanical environment. The purpose of this study is to assess the effect of head length on corrosion of a metal-on-polyethylene articulation. Methods: Retrievals from a single institution of 28-mm cobalt chromium alloy heads with a 12/14 taper from a single manufacturer were studied. Corrosion of femoral head bores were studied utilising a material loss method. Testing was performed using co-ordinate measuring for maximum linear wear depth. Results: 56 heads were examined with lengths of either −3, 0, +4 or +8 mm and all had been in situ for a minimum of 2 years. There were no significant differences in mean maximum linear wear depth (MLWD) ( p = 0.6545). There was no correlation found between MLWD and the time implants were in situ (Spearman coefficient −0.1157) and no significant difference seen between high or standard offset stems ( p = 0.1336). Conclusion: In contrast to studies using qualitative methodologies, there was no correlation between head length and material loss when confined to a 28-mm head. Broad application of this outcome should be cautioned against as this study examined 1 taper construct and a metal-on-polyethylene articulation.
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 | 0.001 | 0.002 |
| 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".