Relationship Between Intensity of Neck Pain and Disability and Shoulder Pain and Disability in Individuals With Subacromial Impingement Symptoms: A Cross-Sectional Study
Bibliographic record
Abstract
OBJECTIVE: This study aimed to verify a possible relationship between shoulder disability and shoulder pain intensity and the variables related to cervical-spine dysfunction, and determine which of these can differentiate moderate to severe shoulder pain (>4 on a numerical rating scale [NRS]) from mild shoulder pain (≤4 on the NRS) in individuals with subacromial impingement symptoms. METHODS: One hundred and forty volunteers with shoulder pain were evaluated. Demographic information and variables related to the shoulder and neck were collected. Self-reported pain and disability of the shoulder and cervical spine were measured using the Shoulder Pain and Disability Index (SPADI) and Neck Disability Index (NDI) questionnaires, respectively. An NRS was used to measure pain in the shoulder and cervical spine. A purposeful modeling strategy was used to determine the best model to predict shoulder disability and shoulder pain (dependent variables). Multiple logistic regression analysis followed by receiver operating curve analysis was used to determine which variables better differentiated moderate to severe shoulder pain from mild shoulder pain. RESULTS: Variables such as Neck Disability Index (NDI) score (β = 1.09, P = .00) and age (β = -0.19, P = .03) were associated with the total SPADI score. Neck pain was significantly associated with shoulder pain (β = 0.40, P = .00). The combination of variables predicting moderate to severe shoulder pain was total SPADI score (odds ratio [OR] = 1.15, P = .003), neck pain (OR = 3.20, P = .04), and age (OR = 1.01, P = .05). CONCLUSION: Our results demonstrate the important connection between shoulder- and neck-related symptoms in individuals with subacromial impingement 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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".