Bibliographic record
Abstract
PURPOSE/BACKGROUND: Tendinopathies are a broad topic that can be examined from the lab to their impact upon function. Improved understanding will serve to bring this pathology to the forefront of discussion, whether in the clinic or the classroom. The purpose of this current concepts clinical commentary is to explore intrinsic and extrinsic mechanisms of rotator cuff (RC) tendinopathy in order to improve clinical and research understanding. METHODS: Pubmed, Medline, Cinahl, PEDro, and Cochrane databases were searched, limiting results to those published in the English language, between the years of 2005 and 2012. The key search terms utilized were intrinsic mechanisms, tendinopathy, stem cells, biologics, platelet-rich plasma (PRP), healing, rotator cuff tears, full-thickness tears, tests, impingement, imaging, ultrasound, Magnetic Resonance Imaging (MRI), radiograph, shoulder advances, treatment, diagnoses, tendon disorders, pathogenesis, matrix metalloproteinase, injections, and RC repair. Over 150 abstracts were reviewed and 43 articles were analyzed for quality and relevance using the University of Alberta Evidence Based Medicine Toolkit. RESULTS/CONCLUSIONS: Current evidence suggests that tendinopathies arise from a multivariate etiology.It is increasingly evident that intrinsic mechanisms play a greater role than extrinsic mechanisms in this process. Emphasis should be placed on patient information (i.e. background information and personal description of symptoms) and imaging/ injection techniques in order to aid in diagnosis. Future treatment technologies such as cell therapy and biological engineering offer the hope of improving patient outcomes and quality of life. LEVEL OF EVIDENCE: Level 5 - Clinical Commentary Related to a Review of Literature.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.015 | 0.012 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".