Greater Perceived Helplessness in Osteoarthritis Predicts Outcome of Joint Replacement Surgery
Bibliographic record
Abstract
OBJECTIVE: To determine if there is a difference between male and female patients in their perceived control of osteoarthritis (OA) symptoms at the time of joint replacement surgery, as measured by the Arthritis Helplessness Index (AHI), and how this helplessness affects surgical outcomes at 1 year. METHODS: From a joint replacement registry, 70 male and 70 female patients were randomly selected and matched for age, body mass index, comorbidity, procedure, and education. Patients completed the AHI prior to surgery. Functional status was assessed at baseline and 1-year followup with the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) score. Linear regression modeling was used to determine the effect of sex on predicting AHI scores. A second model was constructed to examine the effect of AHI on the 1-year WOMAC change score. RESULTS: There were no statistically significant differences in demographic data or clinically significant differences in AHI scores between sexes. Linear regression modeling showed that female sex was a significant predictor of a greater AHI score prior to surgery (p < 0.05). Moreover, a greater AHI score was an independent predictor of a lower WOMAC change score at 1 year (p = 0.01). CONCLUSION: Interventions to improve control over arthritis symptoms should be studied with the goal of improving surgical outcomes.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".