Development of a Core-Stability Model: A Delphi Approach
Bibliographic record
Abstract
CONTEXT: Despite widespread acceptance, there is currently no consensus on the definition, components, and the specific techniques most appropriate to measure and quantify core stability. OBJECTIVE: To develop a comprehensive core-stability model addressing its definition, components, and assessment techniques. DESIGN: Delphi technique. SETTING: University laboratory. PARTICIPANTS: 15 content experts from United States and Canada, representing a variety of disciplines. MAIN OUTCOME MEASURE: The authors distributed an open-ended questionnaire pertaining to a core-stability definition, components, and assessment techniques specific to each expert. They collected data over 2 rounds of telephone interviews. They concluded data collection once a consensus was achieved that equated with 51% agreement among respondents. RESULTS: The authors developed a working definition of core stability as the ability to achieve and sustain control of the trunk region at rest and during precise movement. Eighty-three percent of the experts considered the definition satisfactory. Therefore, the definition was accepted. Furthermore, the experts agreed that muscles (14/15 = 93.3%) and neuromuscular control (8/12 = 66.7%) were components of core stability. Assessment techniques were identified and inconsistencies were highlighted; however, no consensus was established. CONCLUSIONS: A consensus core-stability definition was created and 2 components were identified. However, of the initial definitions provided by the experts, no 2 were identical, which revealed the inconsistencies among experts and the importance of this study. Nonetheless, the goal of obtaining a consensus definition was obtained. Although a consensus for the assessment techniques of core stability could not be reached, it was a beneficial starting point to identify the inconsistencies that were discovered among the content experts.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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".