Clinical Outcomes and Quality of Literature Addressing Glenohumeral Internal Rotation Deficit: A Systematic Review
Bibliographic record
Abstract
BACKGROUND: Glenohumeral internal rotation deficit (GIRD) can negatively impact shoulder function particularly in the throwing athlete. QUESTIONS/PURPOSE: This study aimed to systematically evaluate recent trends in clinical outcomes and quality of published evidence pertaining to GIRD. METHODS: A systematic review was performed in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. PubMed, MEDLINE, PubMed Central, and Embase were searched from January 1, 2011, through April 23, 2017, for all articles evaluating GIRD. Two reviewers independently screened articles for eligibility and extracted data for analysis. RESULTS: = 64) of included studies were level-III to level-V evidence, with no level-I study performed during the study period. Eighty-five percent of studies were either epidemiologic, review, or imaging articles, and only 12% were clinical studies. Significant variability in the clinical definition of GIRD was identified. All studies evaluating non-operative management of GIRD demonstrated significant improvements in internal rotation of the affected extremity. CONCLUSION: Current trends in GIRD-related literature demonstrate limited focus on clinical, therapeutic, or patient-reported outcomes and mostly consist of low-level evidence. There is a lack of consensus in the literature on what clinically constitutes GIRD.
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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 | 0.019 | 0.088 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.009 |
| Bibliometrics | 0.018 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".