Should ‘smart phones’ be used for patient photography?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 it