Inconsistency in the Reporting of Adverse Events in Total Ankle Arthroplasty
Bibliographic record
Abstract
BACKGROUND: Systems for classifying complications have been proposed for many surgical subspecialties. The goal of this systematic review was to analyze the number and frequency of different terms used to identify complications in total ankle arthroplasty. We hypothesized that this terminology would be highly variable, supporting a need for a standardized system of reporting. METHODS: Studies that met predefined inclusion/exclusion criteria were analyzed to identify terminology used to describe adverse events. All terms were then tabulated and quantified with regard to diversity and frequency of use across all included studies. Terms were also grouped into 10 categories, and the number of reported occurrences of each adverse event was calculated. A reporting tool was then developed. RESULTS: Of 572 unique terms used to describe adverse outcomes in 117 studies, 55.9% (320/572) were used in only a single study. The category that was most frequently reported was revision surgery, with 86% of papers reporting on this event using 115 different terms. Other categories included "additional non-revision surgeries" (74% of papers, 93 terms), "loosening/osteolysis" (63% of papers, 86 terms), "fractures" (60% of papers, 53 terms), "wound problems" (52% of papers, 27 terms), "infection" (52% of papers, 27 terms), "implant problems" (50% of papers, 57 terms), "soft tissue injuries" (31% of papers, 30 terms), "heterotopic ossification" (22% of papers, 17 terms), and "pain" (18% of papers, 11 terms). CONCLUSION: The reporting of complications and adverse outcomes for total ankle arthroplasty was highly variable. This lack of consistency impedes the accurate reporting and interpretation of data required for the development of cohesive, evidence-based treatment guidelines for end-stage ankle arthritis. Standardized reporting tools are urgently needed. This study presents a prototype worksheet for the standardized assessment and reporting of adverse events. LEVEL OF EVIDENCE: Level-III, decision analyses, systematic review of Level III studies and above.
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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 | 0.347 | 0.684 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.010 |
| Bibliometrics | 0.022 | 0.021 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".