Risk Stratification Algorithm for Management of Head-Neck Taper Tribocorrosion in Patients with Metal-on-Polyethylene Total Hip Arthroplasty
Bibliographic record
Abstract
ABSTRACT: Adverse local tissue reactions (ALTRs) were initially reported as complications associated with metal-on-metal (MoM) bearings; however, there is increasing concern regarding the occurrence of adverse local tissue reactions from mechanically assisted crevice corrosion (MACC) at the femoral head-neck junction or between other modular junctions of the implant containing cobalt chromium parts in patients with metal-on-polyethylene (MoP) bearings. ALTR due to MACC at the head-neck junction has primarily been reported in association with cobalt chromium alloy femoral heads. As pain following total hip arthroplasty may have various intrinsic and extrinsic causes, a systematic approach to evaluation (risk stratification algorithm) based on the currently available data is recommended to optimize patient management. Evaluation should begin by ruling out common causes of pain, including component loosening and periprosthetic joint infection. While specialized tests such as blood metal analysis and metal artifact reduction sequence magnetic resonance imaging (MARS MRI) are useful modalities in evaluating for ALTRs, over-reliance on any single investigative tool in the clinical decision-making process should be avoided. There should be a low threshold to perform a systematic evaluation for ALTR due to MACC in patients with metal-on-polyethylene total hip arthroplasty as early recognition and diagnosis is critical, as delays in appropriate treatment initiation may result in soft-tissue damage, which complicates surgical treatment and is associated with a higher risk of complications and poorer patient outcomes.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".