Does Alignment in the Hindfoot Radiograph Influence Dynamic Foot-floor Pressures in Ankle and Tibiotalocalcaneal Fusion?
Bibliographic record
Abstract
BACKGROUND: The Saltzman-el-Khoury hindfoot alignment view (HAV) is considered the gold standard for assessing the axis from hindfoot to tibia. However, it is unclear whether radiographic alignment influences dynamic load distribution during gait. QUESTIONS/PURPOSES: We evaluated varus-valgus alignment by the HAV and its influence on dynamic load distribution in ankle and tibiotalocalcaneal (TTC) arthrodesis. PATIENTS AND METHODS: We clinically assessed 98 patients (ankle, 56; TTC, 42) with SF-36 and American Orthopaedic Foot and Ankle Society (AOFAS) scores, visual hindfoot alignment, HAV angle, and dynamic pedobarography using a five-step method. For comparison, 70 normal feet were evaluated. Minimum followup was 2 years (average, 4.11 years; range, 2-6 years). RESULTS: The mean HAV angle was -0.8° ± 7.8° for ankle and -1.2° ± 6.9° for TTC arthrodesis. The HAV angle correlated with pedobarographic load distribution (r = 0.35-0.53). Radiographic alignment did not influence SF-36 or AOFAS scores; however, load distribution correlated to qualities of these scores. Visual alignment only predicted the corresponding HAV angle in 48%. To reproduce the dynamic load of healthy subjects, HAV angles of 5° to 10° valgus were needed. CONCLUSIONS: Visual positioning is inadequate to determine intraoperative positioning and resulted in a varus position with a relatively large SD. The HAV should be used to assess the hindfoot alignment correctly. HAV angles of 5° to 10° valgus are needed to reproduce a physiologic gait pattern.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".