The development of a clinical preventative screening tool for the lower quarter
Bibliographic record
Abstract
Context: There are no concise and thorough lower quarter screening tools found in the literature or the clinical setting. An evaluation tool is needed to identify potential problems that could lead to injury. Purpose: The purpose of this study was to develop a concise and thorough screening tool for the lower quarter. Design: This was a prospective descriptive study following the Modified Delphi Technique, to create a lower quarter screening tool based on opinion from a panel of experts. The dependent variable was the responses ranging from strongly agree to strongly disagree. Setting: This study took place at West Virginia University. Patients or Other Participants: Seven of the eight panelists hold the Certified Athletic Trainer (ATC) credential who have been practicing for over ten years working in the academic setting (37.5%, n=3), athletic training facility (25%, n=2), and the sports medicine/outpatient clinic (37.5%, n=3) for more than 50% of their job. One of the participants is a PT. Five have a MS degree,while 3 are PhD’s. Thirty-seven and a half percent (n=3) hold both PT and ATC credentials. Twelve and a half percent (n=1) are ATC’s and Certified Strength and Conditioning Specialist (CSCS). One participant is an ATC, Board Certified Orthotist (BOCO). The group average is 38.75 publications and presentations on the lower quarter in the past ten years. Criteria for selection were based on publications, presentations and clinical experience with the lower quarter. Interventions: A lower quarter screening tool was created through a series of successive revisions with input being offered from colleagues and a psychometric expert. Two rounds of questionnaires were used prior to the development of the tool. The first round questionnaire was sent via mail to the panel of experts. This panel rated the elements of a lower quarter evaluation on the Likert scale from strongly agree to strongly disagree. A 75% consensus of agreement and a mean score of four met the inclusion criteria. Following the changes and additions made to the questionnaire based on the input from the first round, a second round questionnaire and cover letter was sent again to the panel of experts. This panel again rated the elements of a lower quarter evaluation on the Likert scale from strongly agree to strongly disagree. A 75% consensus and a mean score of four met the inclusion criteria. After the second round material was reviewed and analyzed, a lower quarter screening tool was
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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 | 0.008 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".