Bibliographic record
Abstract
Sir: We read with great interest the recent article by Cano and colleagues1 and the accompanying discussion.2 Although we understand Dr. Hammond’s concerns that “researchers with a specific bias could manipulate the application of the instrument in a manner such that a particular bias is supported,” we perceive that these criticisms are unnecessary and believe that the development and validation of the BREAST-Q represents an important advance for plastic and reconstructive breast surgery. Traditionally, patient-reported outcomes in plastic and reconstructive breast surgery have been assessed mostly using generic and study-specific questionnaires, many of which have not been validated in the breast population and may not be sufficiently sensitive to breast-related concerns, thus risk overestimating treatment effects and leading to reporting bias.3 The BREAST-Q, by contrast, has been specifically designed and validated to assess patient-reported outcomes in plastic and reconstructive breast surgery using rigorous methodology involving patient interviews, focus groups, expert panels, and comprehensive literature reviews to develop a conceptual framework followed by extensive field-testing with 1950 women at five centers in the United States and Canada and the application of Rasch measurement methods to construct scales and traditional psychometric analyses. Indeed, contrary to Dr. Hammond’s concerns, the widespread adoption of the BREAST-Q may result in a significant improvement in both the quality and the consistency of patient-reported outcomes assessment in this area because it will capture concerns specific to women undergoing plastic and reconstructive breast surgery. The issue of reporting bias raised by Dr. Hammond, however, is not restricted to patient-reported outcomes, as recent systematic reviews show that this is common in both clinical and cosmetic studies of plastic and reconstructive breast surgery.4,5 One way to reduce the issues of reporting bias is to develop and use core outcome sets for plastic and reconstructive breast surgery. Core outcome sets are a standardized set of outcomes (usually between seven and 10) that all stakeholders (patients and professionals) agree should be measured and reported as a minimum in all research and audit studies in a particular area. Core outcome sets have been pioneered in the field of rheumatoid arthritis by the Outcome Measures in Rheumatoid Arthritis Clinical Trials initiative (http://www.intermed.med.uottawa.ca/research/omeract./) and now exist in other areas. By providing a minimum standard for outcome reporting, core outcome sets also create greater homogeneity across studies, increasing the value of research by facilitating data synthesis and meta-analysis. The benefits of standardized outcome reporting are already being recognized in oncologic breast surgery. The Breast Reconstruction and Valid Outcomes group is developing a core outcome set for reconstructive breast surgery. Based on literature reviews and work with key stakeholders, a survey has been developed that will evaluate 200 patients’ and 100 health care professionals’ views of the importance of a range of clinical, cosmetic, and patient-reported outcomes in reconstructive breast surgery. Delphi methods will be used to prioritize outcome domains. By doing so, we will develop a methodologic basis for less biased and fairer comparisons between reconstructive procedures while facilitating research synthesis. More importantly, a core outcome set will ensure that the outcomes held in highest regard by patients—not authors—will be integral to all future studies in reconstructive breast surgery. DISCLOSURE The authors have no financial interest to declare in relation to the content of this communication. Joseph A. Ward, M.B.Ch.B. Shelley Potter, M.B.Ch.B., Ph.D. Jane M. Blazeby, M.B.Ch.B., M.D. On behalf of the BRAVO Study Steering Committee Academic Unit of Surgical Research School of Social and Community Medicine University of Bristol Clifton, Bristol, United Kingdom
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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; both teacher heads 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".