Consortium of Orthopaedic Academic Traumatologists: A Model for Collaboration in Orthopaedic Surgery
Bibliographic record
Abstract
In March 2016, North American academic leaders with an interest in and commitment to the field of global orthopaedics met in Orlando, Florida, to gauge each institution's clinical, research, and educational programs in developing countries, establish the main limitations to participating in global health efforts, and assess areas of need for both the participating institutions and their international partners. After this inaugural meeting, a needs assessment survey was distributed to the group to better understand how to organize and unify the individual institutional global efforts. The results revealed that surgeons believed there was a vital need for improved communication, mentorship, and infrastructural support between North American universities. To this end, the Consortium of Orthopaedic Academic Traumatologists (COACT) was founded. The COACT seeks to promote a novel framework geared toward improving trauma care capacity by building collaborative partnerships among leading academic centers across the United States and Canada. The consortium represents a comprehensive partnership that promotes communication, collaboration, and advocacy through a central network to facilitate investigative, educational, and clinical services. Academic partners share best practices, resources, and opportunities in their international outreach projects in low- and middle-income countries in the field of orthopaedic trauma. Over the course of the past 2 years, the COACT has grown to more than 80 faculty, fellow, resident, and student members, representing over 20 orthopaedic institutions across North America.
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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 | 0.035 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.021 | 0.016 |
| Scholarly communication | 0.022 | 0.018 |
| Open science | 0.006 | 0.039 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.014 | 0.004 |
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".