The Impact of Response Shift on Perceived Disability Two Years Following Rotator Cuff Surgery
Bibliographic record
Abstract
BACKGROUND: Understanding how patients appraise their health-related problems is important for an accurate assessment of recovery over time. It has been proposed that the response shift phenomenon has an impact on self-report measures, and this may subsequently affect measurement of true treatment effects. The objective of the present study was to explore the impact of response shift on the subjective perception of disability following rotator cuff surgery. METHODS: We conducted a repeated-measures study of the outcome following surgical decompression or repair of the rotator cuff. The American Shoulder and Elbow Surgeons (ASES) questionnaire was used in a thentest design to measure recalibration response shift two years following surgery. Disability was measured with a disease-specific measure. Objective findings consisting of the range of motion and strength were recorded preoperatively and postoperatively. Parametric and nonparametric univariate and multivariate analyses were performed. The contribution of recall bias was estimated by assessing agreement between pretest and thentest scores on the basis of the percentage of agreement and the Spearman correlation coefficient. RESULTS: One hundred and seven patients (forty-one women and sixty-six men; mean age, fifty-seven years) participated in the study. Seventy-two patients had decompression surgery and forty-four underwent a rotator cuff repair; nine patients had both procedures. A significant response shift was observed in the pain domain (p < 0.0001) and not in the functional ability domain of the ASES (p = 0.58). Three distinct patterns of response shift were observed. Univariate analyses showed that satisfaction with the result of the surgery, the range of motion, strength, age, and response shift were associated with disability. The final multivariate analysis showed that disability at two years following the surgery was affected by the range of motion (p < 0.0001), satisfaction (p = 0.006), age (p = 0.02), and response shift (p = 0.03). CONCLUSIONS: The response shift phenomenon exists in patients who have undergone surgery for rotator cuff disorders and has different patterns related to overrating or underrating of preoperative pain. A recalibration response shift characterized by overrating of preoperative pain was associated with lower levels of residual disability at two years following the surgery.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".