Reply: A Systematic Review of Surgical Randomized Controlled Trials: Part I. Risk of Bias and Outcomes: Common Pitfalls Plastic Surgeons Can Overcome, and A Systematic Review of Surgical Randomized Controlled Trials: Part 2. Funding Source, Conflict of Interest, and Sample Size in Plastic Surgery
Bibliographic record
Abstract
Sir: We enjoyed reading the letter by Drs. Gardiner and Jain regarding our systematic review1,2 and appreciate the opportunity from the Editor to respond. Gardiner and Jain identify a barrier of culture that affects the quality and quantity of randomized controlled trials in plastic surgery. They propose multicenter trainee collaboration as a component of a long-term solution. Among all forms of clinical studies, randomized controlled trials are considered the gold standard for comparing therapeutic interventions when a state of equipoise exists. When comparing surgical procedures, unique challenges are encountered in the conduct of surgical randomized controlled trials that are not present in randomized controlled trials comparing medical interventions (e.g., the cultural resistance to randomization, the challenge of blinding, variability in surgeon skill, and surgical learning curves). We believe the greatest challenge of plastic surgery randomized controlled trials that compare surgical interventions involves recruitment. Problems may be identified at protocol, site, surgeon, and patient levels. Recruitment issues can exist in trial design, where the protocol causes inherent difficulties. Conversely, issues may be specific to individual sites in multicenter trials, where those who are noncompliant or noncommitted limit recruitment. More difficult to address are issues with specific surgeons, where despite agreeing to participate in the trial, they may lack the resources, time, or motivation to fulfill protocol requirements. Enthusiasm to join the trial often disappears with recognition of the effort needed to practically recruit patients; this is known as the Lasagna law.3 Finally, patient-related recruitment issues may exist, where trials require frequent or inconvenient follow-up with limited incentive. The practice of surgery is entrenched in a culture of “treating patients with practices based on rigidly held protocols learned in residency training or opinions presented by leaders in the field.”4 The training and experience needed to become highly skilled tend to create surgeons who favor a certain surgical approach to treat a specific problem. A surgeon’s preference for one surgical technique can make it difficult to convince them to participate in a randomized controlled trials comparing surgical interventions. Furthermore, it may be inappropriate to require a surgeon to perform both interventions; the act of random allocation may in fact reduce the effectiveness of the intervention. We read with interest the initiative Gardiner and Jain describe in the United Kingdom for the creation of a trials network. Large clinical trials networks provide an opportunity to increase the rate of patient enrollment and to increase the generalizability of a randomized controlled trial’s results.5 Clinical trials networks can help to address protocol-related recruitment issues, and potentially patient-related issues, as an individual site may not have the patient volume to complete a large randomized controlled trial. A second opportunity for design and conduct of randomized controlled trials in plastic surgery involves “expertise-based” randomized controlled trial designs, in which the patient is randomized to a surgeon/group of surgeons (experts) committed to performing an intervention.6 With this design, bias from a surgeon’s preference for one surgical technique, which can manifest as differential procedural performance, co-interventions, and subjective outcome assessment is negated. As in conventional randomized controlled trials, plastic surgeons in expertise-based trials cannot be blinded; however, in expertise-based randomized controlled trials, the risk of detection bias is minimized. DISCLOSURE The authors have no financial interest to declare in relation to the content of this communication. Sophocles H. Voineskos, M.D., M.Sc.Christopher J. Coroneos, M.D., M.Sc.Division of Plastic and Reconstructive SurgeryDepartment of Surgery, andSurgical Outcomes Research Centre Achilleas Thoma, M.D., M.Sc.Division of Plastic and Reconstructive SurgeryDepartment of SurgerySurgical Outcomes Research Centre, andDepartment of Clinical Epidemiology and Biostatistics Mohit Bhandari, M.D., Ph.D.Department of Clinical Epidemiology and Biostatistics, andDivision of Orthopaedic SurgeryDepartment of SurgeryMcMaster UniversityHamilton, Ontario, Canada
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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 | 0.067 | 0.371 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.006 | 0.011 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.043 | 0.038 |
| Insufficient payload (model declined to judge) | 0.007 | 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".