Should ‘smart phones’ be used for patient photography?
Bibliographic record
Abstract
BACKGROUND: Within the field of plastic surgery, clinical photography is an essential tool. 'Smart phones' are increasingly being used for photography in medical settings. OBJECTIVE: To determine the prevalence of smart phone use for clinical photography among plastic surgeons and plastic surgery residents in Canada. METHODS: In 2014, a survey was distributed to all members of the Canadian Society of Plastic Surgeons. The questions encompassed four main categories: smart phone use for clinical photos; storage of photos; consent process; and privacy issues. The survey participation rate was 27% (147 of 545) with 103 surgeons and 44 residents. In total, 89.1% (131 of 147) of respondents have taken photographs of patients using smart phones and 57% (74 of 130) store these photos on their phones. In addition, 73% (74 of 102) of respondents store these photos among personal photos. The majority of respondents (75% [106 of 142]) believe obtaining verbal consent before taking clinical photographs is sufficient to ensure privacy is respected. Written consent is not commonly obtained, but 83% (116 of 140) would obtain it, if it could be done more efficiently. Twenty-six percent (31 of 119) of respondents have accidentally shown a clinical photograph on their phone to friends or family. A smart phone application that incorporates a written consent process, and allows photos to be immediately stored externally, is perceived by 59% (83 of 140) to be a possible way to address these issues. CONCLUSION: Smart phones are commonly used to obtain clinical photographs in plastic surgery. There are issues around consent process, storage of photos and privacy that need to be addressed.
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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 | 0.005 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".