Recall Bias in Retrospective Assessment of Preoperative American Shoulder and Elbow Surgeons Scores After Reverse Total Shoulder Arthroplasty
Bibliographic record
Abstract
INTRODUCTION: Although reverse total shoulder arthroplasty (RTSA) has been shown to be effective for the treatment of cuff tear arthropathy (CTA), the patient's inability to accurately recall their preoperative shoulder condition could skew their perception of the effectiveness of the procedure. Identifying patients who are susceptible to notable recall bias before surgery can help surgeons counsel patients regarding expectations after surgery. The purpose of this study was to evaluate whether patients who undergo RTSA are susceptible to recall bias and, if so, which factors are associated with poor recollection. METHODS: Patients who underwent RTSA for CTA by the senior author between September 2016 and September 2018 were identified. All patients completed the American Shoulder and Elbow Surgeons (ASES scores) Standardized Assessment Form at the time of preoperative assessment. Patients were contacted at a minimum of 24 months after surgery to retrospectively assess their preoperative condition. RESULTS: A total of 72 patients with a mean age of 72.2 ± 7.65 years completed a retrospective shoulder assessment at 28.3 ± 7.3 months postoperatively. Patient assessment of shoulder condition showed poor reliability (intraclass correlation coefficient = 0.453, confidence interval, 0.237-0.623). Greater preoperative shoulder ASES scores were associated with a greater difference between preoperative ASES scores and recall ASES scores (β = 0.275, P < 0.001). CONCLUSION: Patients who undergo RTSA for CTA are susceptible to clinically significant recall bias. Patients with better preoperative condition recall worse preoperative shoulder conditions compared with patients with worse preoperative conditions and are susceptible to a higher degree of recall bias. This patient population should be identified preoperatively and have notable counseling before and after surgery to help them better understand their disease burden and what to expect after surgical intervention. LEVEL OF EVIDENCE: III, diagnostic cohort study.
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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 | 0.011 | 0.060 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".