Measuring shoulder function: A systematic review of four questionnaires
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
OBJECTIVE: To conduct a systematic review of the quality and content of the psychometric evidence relating to 4 shoulder disability scales: the Disabilities of the Arm, Shoulder, and Hand (DASH) questionnaire, the Shoulder Pain and Disability Index (SPADI), the American Shoulder and Elbow Surgeons (ASES) score, and the Simple Shoulder Test (SST). METHODS: We conducted a structured search using 3 databases (Medline, CINAHL, EMBase). In total, 71 published primary studies were analyzed. A pair of raters conducted data extraction and critical appraisal using structured tools. A descriptive synthesis was performed. RESULTS: Quality ratings of 55% of the studies reviewed reached a level of > or =75%. Most studies suggest that all 4 questionnaires have excellent reliability (intraclass correlation coefficient > or =0.90). The 4 questionnaires are strongly correlated (r >0.70) with each other and with a number of similar indices, and the questionnaires were able to differentiate between different populations and disability levels. The minimal detectable change (MDC) is approximately 9.4 for the ASES, 10.5 for the DASH, and 18 for the SPADI; the minimal clinically important difference (MCID) is approximately 6.4 for the ASES and 10.2 for the DASH, and ranges between 8 and 13 for the SPADI. MDC and MCID have not been defined for the SST. CONCLUSION: The psychometric properties of the ASES, DASH, and SPADI have been shown to be acceptable for clinical use. Conversely, some properties of the SST still need be evaluated, particularly the absolute errors of measurement. Overall, validation studies have focused on less clinically relevant properties (construct validity or group reliability) than estimates of MDC and MCID.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it