Impact of Before and After Photographs on Parents of Children With Cleft Lip
Bibliographic record
Abstract
Introduction: Cleft lip with or without palate (CL/P) is a common facial deformity requiring surgical intervention whose aesthetic outcomes are typically represented by Before and After Photographs (BAPhotos). With the growing presence of social media, there are concerns about the role of BAPhotos in plastic surgery and their impact on patient’s expectations. Methods: A retrospective analysis of quality assurance survey and interview data was conducted to explore the impact of BAPhotos on parents and their expectations in the context of CL/P. Results: Thirty-five parents were interviewed regarding use of BAPhotos; 91% viewed BAPhotos on the following platforms: Google (n = 26), Facebook (n = 8), Instagram (n = 4), YouTube (n = 4), Snapchat (n = 1), and Other (n = 11). Half of the parents believed that BAPhotos influenced their treatment expectations and these parents were not less satisfied with their child’s surgical outcome than those who did not perceive being influenced (Mann-Whitney U = 124.5, P = .05). A higher proportion of parents who viewed BAPhotos on social platforms felt that their treatment expectations were influenced by BAPhotos (χ 2 , X (df = 1) = 4.49, P = .03). Thematic analysis revealed that parents’ emotional reaction to BAPhotos was shaped by the context of the photos; photos on social platforms that include patient stories (ie, Instagram, Facebook) were more often sources of emotional support. Conclusion: This study furthers our understanding of the impact BAPhotos have on parents of children with CL/P and areas of education regarding the dissemination of BAPhotos which have the potential to positively impact viewing of these photos.
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".