Research Retreat IV: ACL Injuries–The Gender Bias, April 3-5, 2008, Greensboro, NC
Bibliographic record
Abstract
In April 2008, more than 80 attendees from across the United States and Canada participated in the fourth research retreat focused on the gender bias in anterior cruciate ligament (ACL) injury. The retreat was cofounded by Irene Davis, PhD, PT, and Mary Lloyd Ireland, MD, who hosted the 3 previous research retreats in Lexington, Kentucky, in April of 2001, 2003, and 2006. In the first year (2001), a consensus document of what we know, don't know, and still need to know related to this problem was developed.1 Each subsequent retreat has revisited and updated the previous consensus statement as new evidence has emerged.2,3 Over the past 6 years, the number of attendees has grown, and the retreats have attracted some of the foremost nationally and internationally known clinicians and scientists with a common interest in ACL injury. We were pleased to continue this important work by hosting Research Retreat IV in Greensboro, North Carolina.The meeting featured an opening presentation from ACL Retreat cofounder Mary Lloyd Ireland, MD; invited keynote presentations by Scott McLean, PhD, and Bruce Beynnon, PhD — expert scientists well known for their research into factors associated with the gender bias in ACL injury; and 31 fifteen-minute podium presentations of recently completed research relating to the gender bias in ACL injuries. The opening presentation set the stage for the meeting by providing a historical perspective of what research has taught us about the ACL injury gender bias over the past 20 years, and the keynote presentations focused on the current knowledge and theories associated with neuromuscular, biomechanical, anatomical, and hormonal risk factors. The podium presentations were organized into thematic sessions centered on sagittal-plane landing mechanics, sex comparisons in landing and cutting, fatigue and perturbation studies in landing and cutting, anatomical and hormonal factors, and risk factor screening and prevention. Significant time was provided for group discussion after each keynote and each group of podium presentations. At the conclusion of the meeting, participants revisited and updated the consensus statement from the 2006 retreat.2 Following are the consensus statement, keynote presentation summaries, and abstracts organized by topic and presentation order.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.071 | 0.022 |
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".