Scoring systems for the functional assessment of the shoulder
Bibliographic record
Abstract
A number of instruments have been developed to measure the quality of life in patients with various conditions of the shoulder. Older instruments appear to have been developed at a time when little information was available on the appropriate methodology for instrument development. Much progress has been made in this area, and currently an appropriate instrument exists for each of the main conditions of the shoulder. Investigators planning clinical trials should select modern instruments that have been developed with appropriate patient input for item generation and reduction, and established validity and reliability. Among the other factors discussed in this review, responsiveness of an instrument is an important consideration as it can serve to minimize the sample size for a proposed study. The shoulder instruments reviewed include the Rating Sheet for Bankart Repair (Rowe), ASES Shoulder Evaluation Form, UCLA Shoulder Score, The Constant Score, Disabilities of the Arm, Shoulder and Hand (DASH), the Shoulder Rating Questionnaire, the Simple Shoulder Test (SST), the Western Ontario Osteoarthritis of the Shoulder Index (WOOS), the Western Ontario Rotator Cuff Index (WORC), the Western Ontario Shoulder Instability Index (WOSI), Rotator Cuff Quality of Life (RC-QOL), and the Oxford Shoulder Scores (OSS).
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.
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.003 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.015 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.009 |
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".