Thick shells and medially wedged posts increase foot orthoses medial longitudinal arch stiffness: an experimental study
Bibliographic record
Abstract
BACKGROUND: Foot orthoses (FOs) are commonly prescribed devices to attenuate biomechanical deficits and improve physical function in patients with musculoskeletal disorders. It is postulated that FOs provide their effects through the production of reaction forces at the foot-FOs interface. An important parameter to provide these reaction forces is their medial arch stiffness. Preliminary results suggest that adding extrinsic additions to FOs (e.g., rearfoot posts) increases their medial arch stiffness. A better understanding of how FOs medial arch stiffness can be modulated by changing structural factors is necessary to better customise FOs for patients. The objectives of this study were to compare FOs stiffness and force required to lower the FOs medial arch in three thicknesses and two models (with and without medially wedged forefoot-rearfoot posts). METHODS: medial wedge (FO6MW). For each model, three thicknesses (2.6 mm, 3.0 mm, and 3.4 mm) were manufactured. FOs were fixed to a compression plate and vertically loaded over the medial arch at a rate of 10 mm/minute. Two-way ANOVAs and Tukey post-hoc tests with Bonferroni corrections were used to compare medial arch stiffness and force required to lower the arch across conditions. RESULTS: Regardless of the differing shell thicknesses, the overall stiffness was 3.4 times greater for FO6MW compared to mFO (p < 0.001). FOs with 3.4 mm and 3.0 mm thicknesses displayed 1.3- and 1.1- times greater stiffness than FOs with a thickness of 2.6 mm. FOs with a thickness of 3.4 mm also exhibited 1.1 times greater stiffness than FOs with a thickness of 3.0 mm. Overall, the force to lower the medial arch was up to 3.3 times greater for FO6MW than mFO and thicker FOs required greater force (p < 0.001). CONCLUSIONS: medially inclined forefoot-rearfoot posts, and when the shell is thicker. Overall, adding forefoot-rearfoot posts to FOs is significantly more efficient than increasing shell thickness to enhance these variables should that be the therapeutic aim.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".