Minimal Clinically Important Difference and the Effect of Clinical Variables on the Ankle Osteoarthritis Scale in Surgically Treated End-Stage Ankle Arthritis
Bibliographic record
Abstract
BACKGROUND: There is much debate regarding the best outcome tool for use in foot and ankle surgery, specifically in patients with ankle arthritis. The Ankle Osteoarthritis Scale (AOS) is a validated, disease-specific score. The goals of this study were to investigate the clinical performance of the AOS and to determine a minimal clinically important difference (MCID) for it, using a large cohort of 238 patients undergoing surgery for end-stage ankle arthritis. METHODS: Patients treated with total ankle arthroplasty or ankle arthrodesis were prospectively followed for a minimum of two years at a single site. Data on demographics, comorbidities, AOS score, Short Form-36 results, and the relationship between expectations and satisfaction were collected at baseline (preoperatively), at six and twelve months, and then yearly thereafter. A linear regression analysis examined the variables affecting the change in AOS scores between baseline and the two-year follow-up. An MCID in the AOS change score was then determined by employing an anchor question, which asked patients to rate their relief from symptoms after surgery. RESULTS: Surgical treatment of end-stage ankle arthritis resulted in a mean improvement (and standard deviation) of 31.2 ± 22.7 points in the AOS score two years after surgery. The MCID of the AOS change score was a mean of 28.0 ± 17.9 points. The change in AOS score was significantly affected by the preoperative AOS score, smoking, back pain, and age. CONCLUSIONS: Patients undergoing arthroplasty or arthrodesis for end-stage ankle arthritis experienced a mean improvement in AOS score that was greater than the estimated MCID (31.2 versus 28.0 points).
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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 | 0.008 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".