Dyslipidaemia is associated with an increased risk of rotator cuff disease: a systematic review
Bibliographic record
Abstract
Importance Rotator cuff disease affects more than 50% of the population over 60 years of age. It has been suggested that dyslipidaemia is associated with the development of rotator cuff disease. Objective The aim of this review was to present the available literature on the relationship between lipid disorders and rotator cuff disease and to report on the implications of lipid disorders on the surgical management and postoperative healing of rotator cuff tears. Evidence review Medline, Embase and PubMed were searched from inception until 18 January 2017. Studies were screened and data were extracted in duplicate. A methodological assessment was performed for included studies. Findings Nine studies were found to meet the inclusion criteria. Seven of the included studies identified an association between the prevalence of dyslipidaemia and rotator cuff disease. Patients with dyslipidaemia were also found to have more severe rotator cuff tears. Conclusions and relevance The results of this study suggest an association between blood lipid levels and rotator cuff pathology. Specifically, patients with dyslipidaemia are potentially at higher risk for shoulder pain, rotator cuff tears and more severe rotator cuff tears. Further research is required to identify the effect of lipid-lowering medications on the natural history of rotator cuff disease and the impact on conservative and surgical treatment of rotator cuff pathology. Level of evidence LevelsII–IVclinical studies.
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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 | 0.004 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".