Appraisal and patient-reported outcomes following total hip arthroplasty: a longitudinal cohort study
Bibliographic record
Abstract
BACKGROUND: Total hip arthroplasty (THA) is a successful procedure that provides pain relief, restores function, and improves quality of life (QOL) for patients with advanced arthritis in their hip joint. To date, little research has examined the role of cognitive appraisal processes in THA outcomes. This study examined the role of cognitive appraisal processes in THA outcomes in the first year post-surgery. METHODS: This longitudinal cohort study collected data at pre-surgery, 6 weeks post-surgery, 3 months post-surgery, and 12 months post-surgery. Adults (n = 189) with a primary diagnosis of osteoarthritis were consecutively recruited from an active THA practice at a Canadian academic teaching hospital. Measures included the Hip Disability and Osteoarthritis Outcome Score (HOOS), the Mental Component Score (MCS) of the Rand-36, and the Brief Appraisal Inventory (BAI). Analysis of Variance examined the association between BAI items and the HOOS or MCS scores. Random effects models investigated appraisal main effects and appraisal-by-time interactions for selected BAI items. RESULTS: HOOS showed great improvement over the first 12 months after THA, and was mitigated by three appraisal processes in particular: focusing on problems with healthcare or living situation, and preparing one's family for health changes. MCS was stable and low over time, and the following appraisal processes were implicated by very large effect sizes: not comparing themselves to healthier people, focusing on money problems, preparing their family for their health changes, or trying to shed responsibilities. CONCLUSIONS: Appraisal processes are relevant to health outcomes after THA, with different processes coming into play at different points in the recovery trajectory.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".