Toward Larger, More Definitive Trials: A North American Trainee-Led Research Collaborative
Bibliographic record
Abstract
Sir: High-quality randomized controlled trials are lacking in the plastic surgery literature, as indicated by Voineskos et al. in their recent publications: “A Systematic Review of Surgical Randomized Controlled Trials: Part I. Risk of Bias and Outcomes: Common Pitfalls Plastic Surgeons Can Overcome”1 and “A Systematic Review of Surgical Randomized Controlled Trials: Part 2. Funding Source, Conflict of Interest, and Sample Size in Plastic Surgery.”2 As our Canadian colleagues suggested, producing larger, more definitive trials in the field can be accomplished through increased engagement and collaboration.2 One method currently used to accomplish this goal in the United Kingdom is the implementation of trainee-led research collaboratives. The benefits of these networks include large-scale data collection and patient recruitment in a shorter period, decreased repetition, and greater generalizability of findings.3 At present, over 25 trainee-led research networks exist in the United Kingdom spanning multiple specialties. We have taken the lessons learned by our British colleagues to heart. Our team of plastic surgery trainees were the first to adopt the trainee-led research collaborative model in Canada and have been working nationally as a part of the Canadian Plastic Surgery Research Collaborative since September of 2015 (www.cansurg.org). This network has representation from all 13 Canadian academic plastic surgical training programs and their affiliated health centers. Our collaborative consists of over 30 resident physicians, medical students, and attending members. To date, we have completed one national project, and have three other multicenter studies ongoing (Fig. 1).4Fig. 1.: Implementing a multicenter study through a trainee-led research collaborative. ICMJE, International Committee of Medical Journal Editors.Since launching, our neurosurgery colleagues have also adopted this model and have established the Canadian Neurosurgery Research Collaborative. We anticipate that this model will continue to gain popularity as other specialties and countries adopt and implement trainee-led research networks. We hope that this may someday lead to the establishment of a North American collaborative as we work toward a common goal of improving the level of evidence in the plastic surgery literature. DISCLOSURE The authors have no financial interest to declare in relation to the content of this communication. Mona T. Al-Taha, B.B.A.Faculty of Medicine Sarah A. Al Youha, M.D. Osama Samargandi, M.D.Division of Plastic and Reconstructive SurgeryDalhousie UniversityHalifax, Nova Scotia, Canada Helene Retrouvey, M.D.C.M.Division of Plastic and Reconstructive SurgeryUniversity of TorontoToronto, Ontario, Canada Michael Bezuhly, F.R.C.S.C.Division of Plastic and Reconstructive SurgeryDalhousie UniversityHalifax, Nova Scotia, CanadaOn behalf of the Canadian Plastic Surgery ResearchCollaborative
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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 | 0.822 | 0.827 |
| Meta-epidemiology (narrow) | 0.003 | 0.005 |
| Meta-epidemiology (broad) | 0.011 | 0.010 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.012 | 0.019 |
| Scholarly communication | 0.038 | 0.051 |
| Open science | 0.019 | 0.039 |
| Research integrity | 0.041 | 0.060 |
| Insufficient payload (model declined to judge) | 0.012 | 0.006 |
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".