The full radiostereometric analysis migration pattern of tibial components is associated with aseptic loosening
Bibliographic record
Abstract
Aims: Thresholds for acceptable amounts of migration tibial components measured with radiostereometric analysis (RSA) are limited to specific follow-up moments and do not use the full migration pattern. The Michaelis-Menten (MM) model, a non-linear model from biochemistry, could potentially be used to model the entire migration pattern. The purpose of this study was therefore to determine if MM models can be fitted to RSA migration data of tibial components, and if these fitted model parameters can be used for early detection of tibial components at high risk for aseptic loosening. Methods: Migration patterns of tibial component maximum total point motion (MTPM) over six months, one year, and two years, as well as revision rates for aseptic loosening from previously published systematic reviews, were used. Fitted MM models gave the estimated maximal MTPM (MTPMemax) and a constant (K), which is the time in months at which half the MTPMemax is reached for tibial component designs. To assess model fit, intraclass correlation coefficients (ICCs) were calculated for the modelled MTPMemax and reported five-year MTPM values. The estimated MTPMemax and K-values were plotted against their corresponding five-year revision rate for aseptic loosening. Results: For six-month, one-year, and two-year migration patterns, the ICC was 0.81, 0.83, and 0.91, respectively, suggesting excellent agreement between calculated MTPMemax values and the known five-year MTPM values. MTPMemax up to 1.3 mm was considered to be safe based on association with aseptic loosening revision rate, while MTPMemax of more than 1.3 mm was unsafe. The K-value could not be used as a predictor for safe versus unsafe implants. Conclusion: MTPMemax values may be used for early detection of tibial components at high risk for aseptic loosening, possibly offering improvements over the older threshold system.
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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 | 0.014 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".