The role of tibialis posterior fatigue on foot kinematics during walking
Bibliographic record
Abstract
BACKGROUND: The purpose of this study was to investigate the effect of localised tibialis posterior muscle fatigue on foot kinematics during walking. It was hypothesised that following fatigue, subjects would demonstrate greater forefoot and rearfoot motion during walking. It was also postulated that the magnitude of the change in rearfoot motion would be associated with standing anatomical rearfoot posture. METHODS: Twenty-nine subjects underwent an exercise fatigue protocol aimed at reducing the force output of tibialis posterior. An eight camera motion analysis system was used to evaluate 3D foot kinematics during treadmill walking both pre- and post-fatigue. The anatomical rearfoot angle was measured during standing prior to the fatigue protocol using a goniometer. RESULTS: Peak rearfoot eversion remained unchanged following the fatigue protocol. Although increases in rearfoot eversion excursion were observed following fatigue, these changes were of a magnitude of questionable clinical significance (<1.0 degrees ). The magnitude of the change in rearfoot eversion due to fatigue was not associated with the anatomical measurement of standing rearfoot angle. No substantial changes in forefoot kinematics were observed following the fatigue protocol. CONCLUSIONS: These data indicate that reduced force output of the tibialis posterior muscle did not alter rearfoot and forefoot motion during gait. The anatomical structure of the rearfoot was not associated with the dependence of muscular activity that an individual requires to maintain normal rearfoot kinematics during gait.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".