Estimates of Gastrocnemius Muscle Length During Simulated Pathological Gait
Bibliographic record
Abstract
The purpose of this study was to compare estimates of gastrocnemius muscle length (GML) obtained using a segmented versus straight-line model in children. Kinematic data were acquired on eleven typically developing children as they walked under the following conditions: normal gait, crouch gait, equinus gait, and crouch with equinus gait. Maximum and minimum GML, and GML change were calculated using two models: straight-line and segmented. A two-way RMANOVA was used to compare GML characteristics. Results indicated that maximum GML and GML change during simulated pathological gait patterns were influenced by model used to calculate gastrocnemius muscle length (interaction: P = .004 and P = .026). Maximum GML was lower in the simulated gait patterns compared with normal gait (P < .001). Maximum GML was higher with the segmented model compared with the straight-line model (P = .030). Using either model, GML change in equinus gait and crouch with equinus gait was lower compared with normal gait (P < .001). Overall, minimum GML estimated with the segmented model was higher compared with the straight-line model (P < .01). The key findings of our study indicate that GML is significantly affected by both gait pattern and method of estimation. The GML estimates tended to be lower with the straight-line model versus the segmented model.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".