Factors Predictive of Early Complications Following Total Ankle Arthroplasty
Bibliographic record
Abstract
Background: The safety of outpatient total ankle arthroplasty (TAA), and factors predictive of early complications are poorly understood. The objective of this study was to determine the frequency of early complications in patients undergoing outpatient TAA compared to a matched inpatient TAA cohort. Factors predictive of early complications following TAA are elucidated. Methods: A retrospective review of prospectively collected data from the 2011-2018 American College of Surgeons-National Surgical Quality Improvement Program (ACS-NSQIP) database was performed. An unadjusted analysis comparing complication rates in outpatient, and inpatient TAA was performed followed by a propensity score-matched cohort analysis. A multivariate logistic regression model was then used to identify significant independent predictors for complications, reoperation, and readmission following TAA. Results: = .032) in a matched cohort analysis. Thirty-eight (2.6%) patients had a minor complication, with 16 (1.1%) patients having a major complication after TAR. Nineteen (1.3%) patients underwent reoperation, and 42 (2.8%) patients were readmitted within 30 days of the index TAR. Multivariate analysis identified factors predictive of early complications to include length of stay (LOS) >2 days, smoking, hypertension, bleeding disorders, and diabetes mellitus. Conclusion: From this relatively limited data set, outpatient TAA appears to be safe for management of end-stage ankle arthritis in select patients. Inpatient status was associated with an increased rate of 30-day readmission following TAA. Postoperative length of stay >2 days, smoking, hypertension, bleeding disorders, and diabetes mellitus were identified to be associated with early postoperative complications following TAA in this cohort. Level of Evidence: Level III, retrospective cohort study.
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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 | 0.006 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".