The development, calibration and validation of a numerical total knee replacement kinematics simulator considering laxity and unconstrained flexion motions
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Kinematics testing is essential during the development of total knee replacement (TKR) designs. Although computational analysis cannot replace physical testing, it offers repeatability and consistency at a much lower cost and shorter time, making it an excellent complement to experiments. Previous numerical models have been limited by several factors: the validity of the models is usually only considered for a single TKR design, friction models are typically overly simplified and the determination of simulation parameters is often inadequate, or tedious and expensive. The objective of this study is to develop, calibrate and validate a TKR kinematics simulation considering multiple TKR geometries, an accurate friction model and simulation parameters determined using a systematic optimisation method. The calibrated model was able to predict TKR kinematics for different TKR geometries, and is ideal for screening new implant designs, reducing the number of experiments required at the design stage.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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) | 0.000 | 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 it