Using Outcome Measure Results to Facilitate Clinical Decisions the First Year After Total Hip Arthroplasty
Bibliographic record
Abstract
STUDY DESIGN: Variable-occasion, repeated-measures design. OBJECTIVES: To model change in lower extremity functional status of patients 1 year after total hip arthroplasty (THA), using the Lower Extremity Functional Scale (LEFS) and the 6-minute walk test (6MWT), and, secondarily, to provide clinicians with useful data to guide practice. BACKGROUND: Given the prevalence of THA and current resource pressures, standardized outcome measures play an important role in providing physical therapists with objective knowledge about postoperative recovery and prognosis. METHODS: Seventy-five patients, with a mean age of 61 years and a diagnosis of hip osteoarthritis, consented to participate in the study. Assessments were conducted preoperatively and at multiple time points for up to 65 weeks postoperatively. Recovery was modeled using a nonlinear robust regression analysis for clustered data. The predictive ability of age, body mass index, and preoperative score was explored. RESULTS: Gender-based recovery curves were generated to depict the rate and amount of change in LEFS scores and 6MWT distances over the first year. Preoperative baseline 6MWT distance was the only covariate predictive of postarthroplasty 6MWT distances for both males and females. None of the covariates examined were significantly associated with postarthroplasty LEFS scores. CONCLUSION: Although there were variations in the recovery curves by measure, general patterns were noted. There was a rapid increase in both self-reported and physical performance measure scores for 12 to 15 weeks. Thereafter, we observed a slowing of recovery, with a plateau at 30 to 35 weeks for the 6MWT and later for the LEFS. These data can be used to make evidence-based decisions regarding prognosis and to guide the setting of measurable treatment goals. LEVEL OF EVIDENCE: Prognosis, level 1b.
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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 | 0.026 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".