Bibliographic record
Abstract
Multicenter studies are an exception in the orthopedic trauma literature. Colleagues may attempt to organize such a study to obtain large numbers of patients to answer the questions raised. Most of these studies fail to see the light of day, however, because of administrational and monetary problems. An early and notable exception was the LEAP project, which required both NIH funding, and the organizational effort by Michael Bosse and Ellen MacKenzie. Recently, The Canadian Orthopaedic Trauma Association (COTA) was formed as a subspecialty society, with the purpose of improving patient care through cooperative discussions and scientific studies on a national level. Because this organization is intent on answering important questions related to patient care, a scientific vehicle for multicenter clinical research was born. The key to this effort have been cooperation between the various centers throughout Canada. COTA data using this method has already been published in the Journal of Orthopaedic Trauma (Buckley et al). In this issue, Dr. Powell and his colleagues in the Canadian Orthopaedic Trauma Association, have put forth a remarkable effort using all the resources of their group to prospectively evaluate acute respiratory distress syndrome in the polytraumatized patient with a femur fracture. The question raised was whether reaming before insertion of an intramedullary device was contraindicated. Large numbers of patients were required: a multicenter trial was the only way to proceed. A total of 315 patients with 322 femur fractures that met criteria were enrolled in 7 separate participating centers. The data in their manuscript addresses this important question for the orthopedic trauma community. As the international orthopedic trauma community continues to grow, so does our specialty continue to mature. COTA's contribution should be seen as a noteworthy group effort. This method is the logical step to take to obtain the volumes of patients needed to definitively answer important questions. With the growth of national organizations dedicated to orthopedic trauma (as evidenced by the societies that participate in this journal), it is hoped that multicenter studies will now finally appear on a regular basis in our literature. Dr. Powell and his colleagues are to be congratulated for their work. This journal looks forward to many more nationally and internationally based multicenter studies. Roy Sanders, MD Editor-in-Chief
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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 | 0.400 | 0.520 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.017 | 0.010 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.013 | 0.010 |
| Insufficient payload (model declined to judge) | 0.016 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".