Older Patients Can Accurately Recall Their Preoperative Health Status Six Weeks Following Total Hip Arthroplasty
Bibliographic record
Abstract
BACKGROUND: In clinical trials, use of patient recall data would be beneficial when the collection of baseline data is impossible, such as in trauma situations. We investigated the ability of older patients to accurately recall their preoperative quality of life, function, and general health status at six weeks following total hip arthroplasty. METHODS: We randomized consecutive patients who were fifty-five years of age or older into two groups. At each assessment, patients completed self-report questionnaires (at four weeks preoperatively, on the day of surgery, and at six weeks and three months postoperatively for Group 1 and at six weeks and three months postoperatively for Group 2). At six weeks postoperatively, all patients completed the questionnaires on the basis of their recollection of their preoperative health status. We evaluated the validity and reliability of recall ratings, the degree of error in recall ratings, and the effects of the use of recall data on power and sample size requirements. RESULTS: A total of 174 patients (mean age, seventy-one years) who were undergoing either primary or revision total hip arthroplasty were randomized and included in the analysis (118 patients were in Group 1 and fifty-six were in Group 2). Agreement between actual and recalled data was excellent for disease-specific questionnaires (intraclass correlation coefficient, 0.86, 0.87, and 0.88) and moderate for generic health measures (intraclass correlation coefficient, 0.48, 0.58, and 0.60). Increased error associated with recalled ratings compared with actual ratings necessitates minimal increases in sample size or results in small decreases in power. CONCLUSIONS: Patients undergoing total hip arthroplasty can accurately recall their preoperative health status at six weeks postoperatively.
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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 | 0.004 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".