Musculoskeletal modeling and movement simulation for structural hip disorder research: A scoping review of methods, validation, and applications
Bibliographic record
Abstract
, such as hip contact forces and deep muscle activations. While technological advancements in musculoskeletal modeling have increased accessibility, selecting the appropriate modeling approach for a specific research question, particularly when investigating pathological populations, has become more challenging. The purposes of this review were to summarize current modeling and simulation methods in structural hip disorder research, as well as evaluate model validation 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, which either compared multiple modeling methods or applied a single modeling workflow to answer a research question. Findings from studies comparing methods were summarized, such as the effect of generic versus patient-specific modeling techniques on model-estimated hip contact forces or muscle forces. The review also discussed limitations in validation practices, as only 11 of the included studies conducted a validation and used qualitative approaches only. Given the lack of information related to model validation, additional details regarding the development and validation of generic models were retrieved from references and modeling software documentation. To address the wide variability and under-reporting of data collection, data processing, and modeling methods highlighted in this review, we developed a template that researchers can complete and include as a table within the methodology section of their manuscripts. The use of this table will help increase transparency and reporting of essential details related to reproducibility and methods without being limited by word count restrictions. Overall, this review provides a comprehensive synthesis of modeling approaches that can help researchers make modeling decisions and evaluate existing literature.
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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 | 0.067 | 0.191 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.009 |
| Bibliometrics | 0.022 | 0.018 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".