Effect of Femoral Stem Modular Neck’s Material on Metal Ion Release
Bibliographic record
Abstract
Background: In recent decades, the popularity of modular necks in total hip arthroplasty (THA) has increased since modular necks offer the potential to restore the patient’s native anatomy, and thus improve stability. Unfortunately, modular necks are associated with higher complication rates, including implant fracture and modular junction corrosion with adverse local tissue reaction to metal debris. Objective: The objective of this study was to determine the impact of modular neck material on titanium (Ti), chrome (Cr), and cobalt (Co) metal ion levels in patients who underwent a THA with Ti femoral stem, Ti or CrCo modular neck, and ceramic-on-ceramic (CoC) bearing. Methods: Whole blood Ti, Cr, and Co levels were compared at a minimum 1-year follow-up in 36 patients who underwent unilateral, primary CoC large-diameter head THA with Profemur® Preserve modular femoral stems (MicroPort, Arlington, TX, USA). Results: Higher Co concentrations were observed in the CrCo modular neck group (0.46 versus 0.26 µg/l in the Ti neck group, P=0.004) and higher Ti concentrations were observed in the Ti modular neck group (1.98 vs 1.59 µg/l in the CrCo neck group, P=0.007). However, these differences were not clinically meaningful since the absolute values remained within what is considered the safe range of Ti, Cr, and Co ions in whole blood. No patients were re-operated or revised. Conclusion: Modular neck materials had an impact on whole blood metal ion levels but the concentrations measured remained within the safe range at 1-year follow-up. There were no indirect signs of modular junction corrosion with either CrCo or Ti femoral necks.
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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 | 0.000 | 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) | 0.002 | 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".