ACL Research Retreat VI: An update on ACL injury risk and prevention
Bibliographic record
Abstract
It has been well recognized that multiple factors, whether individually or in combination, contribute to noncontact anterior cruciate ligament (ACL) injury. The ongoing mission of the ACL Research Retreat is to bring clinicians and researchers together to present and discuss the most recent advances in ACL injury epidemiology, risk factor identification, and injury-risk screening and prevention strategies and to identify future research directives. The sixth retreat held March 22–24, 2012, in Greensboro, North Carolina, was attended by more than 70 clinicians and researchers, including representatives from Canada, Iceland, Japan, The Netherlands, Norway, and South Africa. The meeting featured keynote presentations and discussion forums by expert scientists in ACL injury risk and prevention and 34 podium and poster presentations by attendees. Keynotes delivered by Ajit Chaudhari, PhD (The Ohio State University), Malcolm Collins, PhD (Medical Research Council and University of Cape Town, South Africa), and Tron Krosshaug, PhD (Oslo Sports Trauma Research Center, Norway) described their ongoing work related to proximal trunk control and lower extremity biomechanics, genetic risk factors associated with ACL injury, and methodologic approaches to understanding ACL loading mechanisms, respectively. Discussion forums led by Jennifer Hootman, PhD, ATC, FNATA, FACSM (Centers for Disease Control and Prevention) and Scott McLean, PhD (University of Michigan), focused on strategies for implementing injury-prevention programs in community settings and took a critical look at the strengths and limitations of motion-capture systems and how we might continue to refine our research approaches to increase the relevance and influence of our biomechanical research, respectively. Podium and poster presentations were organized into thematic sessions of anatomical, genetic, and hormone risk factors; the role of body position in ACL injury risk; pubertal and sex differences in lower extremity biomechanics; injury-risk screening and prevention; and methodologic considerations in risk factor research. Substantial time was provided for group discussion throughout the conference. From these discussions, the 2010 consensus statement1 was updated to reflect recent advances in the field and to chart new directions for future research. Following is the updated consensus statement. \ndoi: 10.4085/1062-6050-47.5.13.\nPMID: 23068597
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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 | 0.010 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.012 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.009 | 0.005 |
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".