Outcomes Vary Significantly Using a Tiered Approach To Define Success After Total Hip Arthroplasty.
Bibliographic record
Abstract
Background: Clinical outcomes following primary total hip arthroplasty (THA) are commonly assessed through patient-reported outcome measures (PROM). The purpose of this study was to use progressively more stringent definitions of success to evaluate clinical outcomes of primary THA at 1-year postoperatively and to determine if demographic variables were associated with achievement of clinical success. Methods: The American Joint Replacement Registry (AJRR) was queried from 2012-2020 for primary THA. Patients that completed the following PROMs preoperatively and 1-year postoperatively were included: Western Ontario and McMaster Universities Arthritis Index (WOMAC), Hip Injury and Osteoarthritis Outcome Score (HOOS) and HOOS for Joint Replacement (HOOS, JR). Mean PROM scores were determined for each visit and between-visit changes were evaluated using paired t-tests. Rates of achievement of minimal clinically important difference (MCID) by distribution-based and anchor-based criteria, patient acceptable symptom state (PASS), and substantial clinical benefit (SCB) were calculated. Logistic regression was used to evaluate associations between demographic variables and odds of success. Results: 7,001 THAs were included. Mean improvement in PROM scores were: HOOS, JR, 37; WOMAC-Pain, 39; WOMAC-Function, 41 (p<0.0001 for all). Rates of achievement of each metric were: distribution-based MCID, 88-93%; anchor-based MCID, 68-90%; PASS, 47-84%; SCB, 68-84%. Age and sex were the most influential demographic factors on achievement of clinical success. Conclusion: .
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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 | 0.010 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 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".