Arch Height Index, Arch Rigidity Index and Arch Stiffness Values in a Symptomatic Population
Bibliographic record
Abstract
BACKGROUND: Normative studies on the Arch Height Index (AHI), Arch Rigidity Index (ARI), and arch stiffness have primarily focused on healthy populations, with little consideration of pathology. The purpose of this study was to create a normative sample of the aforementioned measurements in a pathological sample and to identify relationships between arch structure measurements and pathology. METHODS: AHI was obtained bilaterally at 10% and 90% weightbearing conditions using the Arch Height Index Measurement System (AHIMS). ARI and arch stiffness were calculated using AHI measurements. Dependent t-tests compared right and left, dominant and non-dominant, and injured and non-injured limbs. Measurements of the dominant foot were compared between sexes using independent t-tests. Relationships between arch stiffness and age, sex, and AHI were examined using the coefficient of determination (R2). One-way ANOVAs were used to determine differences between arch structure measurements and number of pathologies or BMI. RESULTS: A total of 110 participants reported either one (n=55), two (n=38), or three or more (n=17) pathologies. Plantar fasciitis (n=31) and hallux valgus (n=28) were the most commonly reported primary concerns. AHI, ARI, and arch stiffness did not differ between limbs for any comparisons, nor between sexes. Between subgroups of BMI and number of pathologies, no differences exist in AHI or ARI; however, BMI was found to have an impact on AHI (10%WB) and arch stiffness (p<.05). Arch stiffness showed a weak relationship to AHI, where a higher AHI was associated with a stiffer arch (R2=0.06). CONCLUSIONS: Normative AHI, ARI and arch stiffness values were established in a pathological sample with a large incidence of plantar fasciitis and hallux valgus. Findings suggest relationships between arch stiffness and both BMI and arch height; however, few trends were noted in AHI and ARI. Determining relationships between arch structure and pathology is helpful for both clinicians and researchers.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".