More than pain and physical limitation: the declined cognitive performance associated with rotator cuff injuries
Bibliographic record
Abstract
BACKGROUND: Rotator cuff (RC) injuries often lead to shoulder pain, physical limitations, sleep disturbances, and emotional distress. However, the relationship between these symptoms and cognitive decline remains unclear. METHODS: We recruited 150 patients with RC injury, who completed the Visual Analog Scale (VAS) for pain, the American Shoulder and Elbow Surgeons scale (ASES), the Pittsburgh Sleep Quality Index (PSQI), the Beck Depression Inventory II (BDI-II), and the State-Trait Anxiety Inventory (STAI). Cognitive function was assessed using the Montreal Cognitive Assessment (MoCA), Digit Symbol Substitution Test (DSST), Trail Making Test (TMT), and Digit Span. RESULTS: High prevalence rates of potential MCI (54% based on MoCA) and sleep disturbance (64.7%) were found. Depressive and anxiety symptoms were present in 18% and 28-30%, respectively. Multiple regressions indicated that older age, lower education, poorer sleep quality, and higher state anxiety were significant predictors across various cognitive tests, but not the pain intensity. Instead, logistic regression confirmed that older age (OR = 1.08, 95% CI: 1.04-1.12) and lower shoulder function (OR = 0.97, 95% CI: 0.95-0.99) significantly increased MCI risk. DISCUSSION: This study highlights that while pain intensity itself was not a predictor of cognitive decline, other associated symptoms like sleep disturbances and emotional distress contribute to poor cognitive function. Furthermore, the physical limitations resulting from RC injuries increase the risk of mild cognitive impairment (MCI). Management of RC injuries should thus address cognitive factors alongside physical and psychological symptoms.
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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 | 0.001 | 0.004 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".