Evaluation of the Method for Analyzing Chromium, Cobalt and Titanium Ion Levels in the Blood Following Hip Replacement with a Metal-on-Metal Prosthesis
Bibliographic record
Abstract
High-resolution inductively coupled plasma mass spectrometry (HR-ICP-MS) can be used to measure metal ion levels in whole blood, although the accuracy and repeatability of this method is not yet known in the clinical setting. In this study, chromium, cobalt and titanium ion levels were measured in three whole blood samples, each collected at the same moment from 101 patients undergoing total hip arthroplasty with a metal-on-metal (MoM) bearing. The first sample (purge sample) had direct contact with the metal needle used during insertion of the catheter, whereas the second sample (reference sample) and the third sample (reserve sample) did not. The absolute difference between reference samples and reserve samples was greater than the limit of quantification of the HR-ICP-MS device for all three ions (0.84 versus 0.35 µg/L for chromium, 0.74 versus 0.07 for cobalt, and 0.88 versus 0.70 µg/L for titanium), although the levels were very small in most cases, they exceeded the clinical significant threshold in 19 to 31% of the cases. No clinically significant difference was observed between reference samples and purge samples. Therefore, HR-ICP-MS is a clinically acceptable method to evaluate metal ion levels in blood following hip replacement with an MoM prosthesis, and it is not necessary to discard the purge sample to obtain repeatable results.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".