Characteristics of Stabilizer Muscles: A Systematic Review
Bibliographic record
Abstract
PURPOSE: To identify the main characteristics, based on available evidence, of stabilizer muscles to inform the development of a definition of stabilizer muscles. METHODS: Electronic databases were systematically searched for relevant literature from the databases' inception to June 2013 using keywords related to stability, muscles, and characteristics of stabilizer muscles. Studies that provided at least one characteristic of a stabilizer muscle were included. For the quality assessment, all included articles were categorized as either experimental or opinion-based studies. Methodological quality was assessed using a customized checklist, and data were analyzed with a narrative synthesis involving content analysis. The number of articles providing either direct evidence supporting a link between the characteristic and joint stability or indirect evidence that a muscle considered to be a stabilizer has that characteristic determined the level of significance of that characteristic for stabilizer muscles. RESULTS: A total of 77 studies met the inclusion criteria. The highest number of articles providing supporting evidence that a particular muscle characteristic plays a stabilizing role related to biomechanical characteristics (27 articles), followed by neurological characteristics (22 articles) and anatomical/physiological characteristics (4 articles). CONCLUSION: Based on a synthesis of supporting evidence from the literature, stabilizer muscles can be defined as muscles that contribute to joint stiffness by co-contraction and show an early onset of activation in response to perturbation via either a feed-forward or a feedback control mechanism. These results may guide researchers to investigate which muscles exhibit these characteristics to determine whether particular muscles have a stabilizer rather than a prime mover role during normal functioning.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".