Increasing age does not influence hip-specific functional outcome or health-related quality of life following total hip arthroplasty
Bibliographic record
Abstract
AIMS: The primary aim of our study was to assess the influence of age on hip-specific outcome following total hip arthroplasty (THA). Secondary aims were to assess health-related quality of life (HRQoL) and level of activity according to age. METHODS: A prospective cohort study was conducted. All patients were fitted with an Exeter stem with a 32 mm head on highly cross-linked polyethylene (X3RimFit) cemented acetabulum. Patients were recruited into three age groups: < 65 years, 65 to 74 years, and ≥ 75 years, and assessed preoperatively and at three, 12, 24, and 60 months postoperatively. Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC), Harris Hip Score (HHS), and Hip disability and Osteoarthritis Outcome Score (HOOS), were used to assess hip-specific outcome. EuroQol five-dimension five-level questionnaire (EQ-5D-5L) and 36-Item Short Form Survey (SF-36) scores were used to assess HRQoL. The Lower Extremity Activity Scale (LEAS) and Timed Up and Go (TUG) were used to assess level of activity. RESULTS: There were no significant (p > 0.05) differences in the WOMAC scores, HSS, HOOS, or EQ-5D-5L at any postoperative timepoint between the age groups. Patients aged ≥ 75 years had significantly lower physical function (p ≤ 0.010) and physical role (p ≤ 0.047) SF-36 scores at 12, 24, and 60 months, but were equal to that expect of an age-matched population. No differences according to age were observed for the other six domains of the SF-36 (p > 0.060). The ≥ 75 years group had a lower LEAS (p < 0.001) and longer TUG test times (p ≤ 0.032) compared to the < 65 years group, but older age groups had significant (p < 0.001) improvement relative to their preoperative baseline measures. CONCLUSION: 2022;3(9):692-700.
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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 | 0.004 |
| 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) | 0.002 | 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".