Scapular Kinematic During Arm Elevation in Shoulder Impingement Syndrome Using Motion Analysis System: A Systematic Review and Meta-analysis
Bibliographic record
Abstract
Context: With a key role in normal shoulder function, scapular kinematics have been investigated in shoulder impingement syndrome (SIS). Objectives: This systematic review aimed at determining scapular kinematic patterns in patients with SIS compared to in asymptomatic individuals. Data Sources: Databases such as PubMed, Scopus, Web of Science, Ovid, Embase and PEDRO were searched from January 1995 to June 2021. Study Selection: Articles in English published in peer-reviewed journals and using motion analysis systems to compare scapular kinematics between patients with SIS and asymptomatic subjects during arm elevation were included. Data Extraction: A modified Downs and Black checklist was used to assess the risk of bias of the included studies. A random-effects model was employed to perform a meta-analysis. Results: Nine out of 1650 screened abstracts were included for data extraction. Scapular upward rotation significantly decreased during arm elevation in SIS (SMD = -0.13, 95% CI = -0.23 to -0.02) with a low effect size (I2 = 46%). No differences were observed in scapular posterior tilt (SMD = -0.07, 95% CI = -0.18 to 0.03) and external rotation (SMD = 0.02, 95% CI = -0.06 to 0.09) between patients with SIS and asymptomatic subjects. Conclusions: This review revealed that except for scapular upward rotation, scapular movement was generally insignificantly different between the subjects with and without SIS during arm elevation. Between-group differences might have been overlooked as a result of the high risk of bias in the included studies. The high-quality studies addressing confounders are required to provide a definitive conclusion on the relationship between SIS and scapular kinematics.
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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 | 0.014 | 0.036 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.030 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".