Assessment of Anatomical Representations of the Trunk Muscles in EMG-Assisted Spinal Load Models
Bibliographic record
Abstract
This study evaluated the differences in biomechanical model performance and predicted spinal loading on L5/S1 for controlled sagittally symmetric lifting exertions, utilizing three different EMG-assisted biomechanical models comprising different torso anatomical inputs. The first model was based on muscle lines-of-action and prediction of cross-sectional areas from an historical biomechanical model. The second model was based upon observations from an MRI study on torso geometry. The third model consisted of a hybrid approach, where geometric inputs from MRI observations were supplemented with lines-of-action observed from cadaver studies, where necessary. To evaluate the models, spinal loading and model performance was evaluated for thirty-five males performing sagittally symmetric lifting tasks starting from 55 deg trunk flexion to upright neutral. All three models resulted in comparable and acceptable model performance. However, differences were observed in predicted spinal loading. The hybrid model resulted in smaller compression force and sagittal plane shear force when compared to the MRI-only model, and smaller sagittal plane shear forces when compared to the historical biomechanical model. These results indicate that the geometric representation of the torso anatomy needs to be considered when evaluating spinal loading predicted from different biomechanical models for comparison to tolerance estimates in attempts to evaluate risk of injury.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.000 |
| 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".