An Updated Systematic Review of Turnout Position Assessment Protocols Used in Dance Medicine and Science Research
Bibliographic record
Abstract
Turnout, or external rotation of the lower limbs, is an integral part of classical ballet technique. Contributions of lower limb structures to turnout can be separated into HER (hip external rotation) and NHCTO (non-hip contributions to turnout). This study aimed to review systematically methods used to measure turnout in dance medicine and science research, thereby updating the literature since the Champion and Chatfield review of 2008. CINAHL, EMBASE, PubMed, and Web of Science were searched in January 2018 by two independent reviewers. Peer-reviewed studies measuring turnout in dance were included, except those published prior to March 23, 2006, as that was the last date of publication included in the previous review. Abstracts, theses, and editorials were excluded. From each study, study design, population (sample size, sex, age, genre of dance, and level of training), details of the protocol used, and result of turnout measurement were extracted, as well as reliability data. All included studies were assessed for risk of bias, using either Newcastle-Ottawa scale, AXIS tool, or PEDro scale as appropriate for each study design. A total of 41 studies met the inclusion criteria. Twenty-eight studies measured HER, nine measured NCHTO, and 22 measured total turnout (TTO). An increased number of studies investigated TTO (N = 22; N = 4 passive TTO) and NHCTO (N = 9) since 2006. All studies scored above half the points attainable from their respective tools. Results suggest HER remains the most common protocol for measuring turnout (N = 28), despite the fact it disregards input from structures below the hip. It is concluded that researchers should focus on quality of reporting of protocols to ensure repeatability and facilitate comparison of results. Future studies should include absolute reliability and validity testing of all currently used protocols so that standardization can be fully achieved.
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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 | 0.054 | 0.194 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.011 | 0.010 |
| Bibliometrics | 0.029 | 0.024 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".