Can We Reach a Consensus on the Appropriate Use of Before and After Photos in Breast Surgery?
Bibliographic record
Abstract
Breast surgery is an area of practice where patients value before and after photographs (BAPs). Consensus is needed to develop guidelines to address the deficit in the literature regarding appropriate use of BAPs, as these may ultimately play a significant role in the breast surgery consent process. METHODS: Expert breast reconstructive surgeons participated in a modified nominal group technique (NGT) to establish expert consensus on categories and criteria to be used when evaluating appropriate use of BAPs as part of informed consent. Endorsement rate of 75% and coefficients of variance within and between rounds were conducted to determine validity of each criteria item's rank order. RESULTS: Eight experts participated in the NGT in-person meeting and subsequent online survey. five of seven categories were endorsed for discussion: purpose, image type, anatomy, results, and photographic integrity. Overall consensus was obtained for six of 11 criteria. Criteria items found to have consensus were: patients considering surgery being the intended photograph audience (100% endorsement, CV1 - CV2 = 0.01), use of photographic images (75% endorsement, CV1 - CV2 = 0.04), defining the standard clinical photograph by having patients in the same body position (100% endorsement, CV1 - CV2 = 0.14), anonymizing images by removing all digital tags (88% endorsement, CV1 - CV2 = 0.03) and patient identifiers (75% endorsement, CV1 - CV2 = 0.00), not limiting the number of photograph sets needed for sufficient representation (100% endorsement, CV1 - CV2 = 0.07), and representing average outcomes (100%, CV1 - CV2 = 0.06). CONCLUSIONS: Early use of this validated and effective technique helps identify potential consensus categories and criteria that surgeons recommend for the use of BAPs in the informed consent process. Further study is required.
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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 | 0.213 | 0.412 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.009 | 0.020 |
| Open science | 0.006 | 0.013 |
| Research integrity | 0.009 | 0.010 |
| Insufficient payload (model declined to judge) | 0.006 | 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".