Musculoskeletal Modeling and Movement Simulation for Structural Hip Disorder Research: A Scoping Review of Methods and Applications
Bibliographic record
Abstract
Abstract Musculoskeletal modeling is a powerful tool to quantify biomechanical factors typically not feasible to measure in vivo, such as hip contact forces and deep muscle activations. The purposes of this review were to summarize current modeling and simulation methods in structural hip disorder research and evaluate model validation practices and study reproducibility. MEDLINE and Web of Science were searched to identify literature relating to the use of musculoskeletal models to investigate structural hip disorders (i.e., involving a bony abnormality of the pelvis, femur, or both). Forty-seven articles were included for analysis. Studies either compared multiple modeling methods or applied a single modeling workflow to answer a research question. Overall, differences in outputs were shown between generic models scaled to participants’ anthropometrics and models with additional patient-specific geometry; however, generic models were most commonly used in application studies. The 11 studies that assessed model validation used qualitative approaches only. There was also wide variability and under-reporting of data collection, data processing, and modeling methods. Common assumptions made in musculoskeletal modeling during the model development, validation, and movement simulations were identified that are important to consider when evaluating the clinical applicability of modeling predictions in patients with structural hip disorders. Differences between generic and patient-specific model outputs exist; however, whether the patient-specific models are more accurate is still unknown. Increased transparency in reporting of data collection, signal processing, and modeling methods is needed to increase study reproducibility and allow for better assessment of modeling 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 | 0.029 | 0.094 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.019 | 0.015 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".