VALIDITY OF FUNCTIONAL SCREENING TESTS TO PREDICT LOST-TIME LOWER QUARTER INJURY IN A COHORT OF FEMALE COLLEGIATE ATHLETES
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: Lower quarter injuries account for more than 50% of all injuries in collegiate athletics. Neuromuscular screening tests could potentially identify athletes who are at risk for sustaining an injury. While previous research has studied individual tests, the authors of this paper are unaware of any study that has compared diagnostic accuracy of multiple neuromuscular screening tests within one study cohort. HYPOTHESIS/PURPOSE: The purpose of this study was to examine the accuracy of three common neuromuscular screening tests to predict the occurrence of a lower quarter injury in female collegiate volleyball and basketball players. STUDY DESIGN: Prospective Cohort. METHODS: , and Single Leg Hop test. Data were collected on lower quarter injury incidence, lost practice time, and lost competition time among subjects throughout the course of one season. Receiver operating characteristics curves were plotted and area under the curve was calculated to assess the relationship between lower extremity injury incidence and the scores of the functional tests. RESULTS: Lost-time injuries occurred in 11 athletes (31.4%), of whom, six athletes (17.1%) lost 50 hours or greater. There were no significant relationships between occurrence of a lost-time lower extremity injury and scores on any of the three tests. Positive and negative likelihood ratios all included the value of 1.0. CONCLUSIONS: Although reliable, the screening tests under study did not appear to retain adequate validity to predict lower quarter injury risk within these female collegiate athletes. LEVEL OF EVIDENCE: Level 2b.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.001 | 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 it