Understanding the Appropriate and Beneficial Use of Before and After Photos in Breast Surgery: A North American Survey
Bibliographic record
Abstract
Introduction: Before and after photographs (BAPs) in breast surgery have been identified as important components of the informed consent process. Currently, there is limited consensus on the contents and presentation of BAPs. This study collected the opinions of prior and prospective patients on this topic. Methods: A survey, based on criteria identified by our previous nominal group technique (NGT) study, was designed to obtain patient perspectives on BAPs in breast surgery. Amazon Mechanical Turk, a validated crowd-sourcing tool, was used to identify and survey a group of 72 participants who indicated that they had undergone or were planning to undergo breast surgery. Likert items were analyzed using either chi-squared or Fisher's exact test. Results: Most respondents were cis-gendered-women (89%), Caucasian (83%), and between 31 and 41 years old (38%). Respondents agreed that BAPs are important to the consent process, for enabling patient-centered care, and should be presented in standardized sets. BAPs should be more accessible through different platforms, display multiple time points to show the healing process, and have multiple views including close-ups of scars. Photos should be unaltered except for de-identification, and have more diversity with regard to patient gender, age, skin color, and body mass index. These results align with results from our NGT study. Conclusion: Through this study we have identified many criteria that BAPs should meet according to prior and prospective breast surgery patients. Surgeons should think critically about how they present BAPs during the consent process to ensure effective patient-centered care.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".