The Demographics of Total Ankle Replacement in the USA: A Study of 21,222 Cases Undergoing Pre- Operative CT Scan-Based Planning
Bibliographic record
Abstract
Category: Ankle; Ankle Arthritis Introduction/Purpose: To understand the role of total ankle replacement (TAR) in treating the spectrum of arthritis of the ankle, a clear understanding of the epidemiology of ankle arthritis is required. The largest pools of epidemiologic data available to date come from international registries. In the USA, the largest market for TAR, where an estimated 10,000 TARs are implanted per year, the largest pool of demographic data on patients undergoing TAR is comprised of just 805 cases collected over 6 years. With the advent of patient-specific instrumentation (PSI), detailed demographic and CT scan data can now be collected. These data on 21,222 cases undergoing CT scan-based PSI planning were reviewed to define the demographics of a very large cohort of TAR patients. Methods: The cohort contained 21,222 patients from the USA and Canada, with surgery dates from 2012 - 2019. Data analysed included deformity measures, presence of existing hardware and joint fusion status. To date, a subset sample of 4800 cases was available for analysis. Extraction is ongoing and data for the full cohort will soon be available. This subset described cases with surgery dates ranging from November 2015 through May 2019. Summary statistics to describe age, gender, ankle size, and tibio- talar deformity were calculated. Of the 4800 patients analyzed, 53% were male. Mean age 63.6 years (SD 10.4) (Age distribution in Figure 1a). The deformity distribution is shown in Figure 1b, with varus more common than valgus. The mean degree of deformity increased with every decade of patient age from 6.1° (age 30-39) to 9.2° (age 80-89), and over time from 9.3°(2016) to 11.8° (2019) [in stemmed- implant cases]. Results: Tibia size varied with gender. Females ranged between 34-38mm in 85% of cases; males from 41-48mm in 79%. Of 21,222 cases, 5964 (28%) had adjacent hardware (screws, etc) in situ and pre-existing ankle fusions were present in 517 (2.4%), increasing from 1.2% in 2013 to 2.9% in 2019.The mean age of TAR patients is similar to that reported in smaller series. Tibia size was significantly greater in males than females, a finding not previously reported in demographic literature. In contrast to knee arthritis, intra-articular deformity >5° is common, present in > 51% of cases (varus > valgus). This is the first series to show the degree of deformity increases with age. Over time, TAR is being used in cases with greater deformity. Conclusion: Hardware is seen to be commonly present in TAR, increasing complexity. Conversion of fusion to TAR, while rare, is more common than existing literature suggests, with the rate increasing each year, suggesting this may be an increasingly important role for TAR in the future. This study presents the largest set of demographic data on TAR patients in the literature. The demographics of USA patients undergoing TAR are similar to those seen in non-USA registries. Deformity is common, increasing with age. The severity of deformity treated with TAR and conversion of fusion to TAR are increasing over time.
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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 | 0.001 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".