The validation of the visual analogue scale for patient satisfaction after total hip arthroplasty
Bibliographic record
Abstract
INTRODUCTION: Patient satisfaction becomes more important in our modern health care system. The assessment of satisfaction is difficult because it is a multifactorial item for which no golden standard exists. One of the potential methods of measuring satisfaction is by using the well-known visual analogue scale (VAS). In this study, we validated VAS for satisfaction. PATIENT AND METHODS: In this prospective study, we studied 147 patients (153 hips). The construct validity was measured using the Spearman correlation test that compares the satisfaction VAS with the Harris hip score, pain VAS at rest and during activity, Oxford hip score, Short Form 36 and Western Ontario McMaster Universities Osteoarthritis Index. The reliability was tested using the intra-class coefficient. RESULTS: The Pearson correlation test showed correlations in the range of 0.40-0.80. The satisfaction VAS had a high correlation between the pain VAS and Oxford hip score, which could mean that pain is one of the most important factors in patient satisfaction. The intra-class coefficient was 0.95. CONCLUSIONS: There is a moderate to mark degree of correlation between the satisfaction VAS and the currently available subjective and objective scoring systems. The intra-class coefficient of 0.95 indicates an excellent test-retest reliability. The VAS satisfaction is a simple instrument to quantify the satisfaction of a patient after total hip arthroplasty. In this study, we showed that the satisfaction VAS has a good validity and reliability.
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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 | 0.009 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".