Establishing User Error on the Patient-Reported Component of the American Shoulder and Elbow Surgeons Shoulder Score
Bibliographic record
Abstract
BACKGROUND: The American Shoulder and Elbow Surgeons (ASES) score is a patient-reported outcome (PRO) questionnaire developed to facilitate communication among international investigators and to allow comparison of outcomes for patients with shoulder disabilities. Although this PRO measure has been deemed easy to read and understand, patients may make mistakes when completing the questionnaire. PURPOSE: To evaluate the frequency of potential mistakes made by patients completing the ASES score. STUDY DESIGN: Cross-sectional study; Level of evidence, 3. METHODS: A prospective cross-sectional study was performed for 600 ASES questionnaires completed by patients upon their first visit to 1 of 2 clinic locations (Australian vs Canadian site). Two categories of potential errors were predefined, and then differences in error rates were compared based on demographics (age, sex, and location). To determine whether these methods were reliable, an independent, third reviewer evaluated a subset of questionnaires separately. The interrater reliability was evaluated through use of the Cohen kappa. RESULTS: The mean patient age was 49.9 years, and 63% of patients were male. The Cohen kappa was high for both evaluation methods used, at 0.831 and 0.918. On average, 17.9% of patients made at least 1 potential mistake, while an additional 10.4% of patients corrected their own mistakes. No differences in total error rate were found based on baseline demographics. Canadians and Australians had similar rates of error. CONCLUSION: To ensure the accuracy of the ASES score, this questionnaire should be double checked, as potential mistakes are too frequently made. This attentiveness will ensure that the ASES score remains a valid, reliable, and responsive tool to be used for further shoulder research.
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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 | 0.029 | 0.096 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".