A scoping review and evaluation of open-source transtibial amputation musculoskeletal models for female populations
Bibliographic record
Abstract
Musculoskeletal modeling is often used to study people with transtibial amputations. Females in this population are of particular interest as they are underrepresented in research, experience unique challenges, and demonstrate gait biomechanics distinct from males. Because generic models often neglect innate variations between populations, it is important to determine whether data used to develop a model are representative of the population studied. The objective of this study was to review and analyze existing transtibial amputation musculoskeletal models, establish a database from the information compiled, and use the database to select the model most relevant for studying female populations. A scoping search was performed and a database was created based on data detailing the eligible models. Models were evaluated through a weighted decision process based on criteria of their representation of females with transtibial amputations, prosthetic functionality, development transparency, overall functionality, and experimental validation methods. The scoping review identified 3 studies, Willson et al., LaPrè et al., and Miller and Esposito. A database detailing these models was established. The Willson model scored highest on all criteria except overall functionality, where the LaPrè model outscored it. Based on the established weightings, the Willson model was classed most appropriate for the stated goals. The created database can be used by other researchers to guide their own modeling studies, irrespective of the population of focus. Of the 3, the Willson model was found most relevant for studying females with transtibial amputations. This model will be used in future work investigating and addressing challenges of females with transtibial amputations.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".