An Analysis of Shoulder Outcomes Scores in 275 Consecutive Patients: Disease-Specific Correlation Across Multiple Shoulder Conditions
Bibliographic record
Abstract
OBJECTIVES: To determine the outcomes scores of military patients who initially present with a variety of shoulder conditions, identify which scores demonstrate the highest correlation per diagnosis, and determine if a difference exists for patients who went onto surgery. METHODS: Two-hundred and seventy five consecutive patients with mean age of 36.5 +/- 12.9 at presentation completed baseline outcomes assessments that included Single Assessment Numeric Evaluation (SANE), American Shoulder and Elbow Surgeons (ASES) Score, Western Ontario Shoulder Instability Index (WOSI), Western Ontario Rotator Cuff Index (WORC), the Simple Shoulder Test (SST), and the Disabilities of the Arm, Shoulder, and Hand Index (DASH). The patients were grouped by clinical, radiographic, and surgical findings into 10 diagnostic categories. OUTCOMES: The initial mean outcomes scores were SANE 48.8, ASES 50.1, WOSI 1279 (40% normal), WORC 1122.4 (47% normal), SST 6.7, and DASH 33.1. Patients with superior labrum anterior-posterior tears demonstrated the lowest mean scores, followed by instability and rotator cuff tear patients. For all conditions, scores were lower for patients who went onto surgery compared with those managed nonoperatively (p = 0.008). CONCLUSIONS: Our findings may be utilized as a baseline to compare and track patient-derived disability across multiple shoulder conditions and serve to define mean diagnosis-specific shoulder patient preoperative scores.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".