Sagittal Distal Tibial Articular Angle and the Relationship to Talar Subluxation in Total Ankle Arthroplasty
Bibliographic record
Abstract
BACKGROUND: Longevity of total ankle replacement (TAR) depends heavily on anatomic alignment. The lateral talar station (LTS) classifies the sagittal position of the talus relative to the tibia. We hypothesized that correcting the sagittal distal tibial articular angle (sDTAA) during TAR would anatomically realign the tibiotalar joint and potentially reduce the risk of prosthesis subluxation. METHODS: The LTS (millimeters) and sDTAA (degrees) were measured twice by 2 blinded observers using weight-bearing lateral ankle radiographs obtained before (n = 96) and after (n = 94) TAR, with excellent interobserver and intraobserver reliability (correlation coefficient >0.9). RESULTS: Preoperative LTS was as follows: anterior (60.4%), posterior (27.1%), and neutral (12.5%). A strong preoperative correlation was found between LTS and sDTAA (r = 0.81; P < .0001). In ankles that were initially anterior and became less anterior postoperatively (n = 41), LTS decreased from an average 8.1 mm to 6.5 mm and the LTS changed 1.1 mm per degree of sDTAA change. In ankles that were initially posterior (n = 25), LTS increased from an average of -5.1 mm to -2.8 mm and the LTS changed 0.6 mm per degree of sDTAA change. The correlation between LTS and sDTAA was reduced postoperatively (r = 0.62; P < .0001). CONCLUSIONS: Our results suggest that rather than following generic recommendations, the surgeon should customize the sagittal distal tibial cut to the individual patient based on the preoperative LTS in order to achieve neutral TAR alignment. LEVEL OF EVIDENCE: Level III, retrospective comparative series.
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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 | 0.007 |
| 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".