Medical Photography Usage Amongst Doctors at a Portuguese Hospital
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
Technological advancements in smartphones have made it possible to create high-quality medical photographs, with the potential to revolutionise patient care. To ensure the security of the patient’s data, it is important that medical professionals receive informed consent from the patient, that physical conditions are met to take a photograph, and that these medical images are stored correctly. This study aimed to determine if medical professionals of an academic hospital make use of medical photography, and how the content is obtained, stored, transferred, and used. Methods: A 30-question questionnaire was distributed across 29 medical departments at Centro Hospitalar Universitário de São João (CHUSJ), a tertiary referral and teaching hospital in Porto, Portugal, with approximately 900 medical professionals. Quantitative statistical methods were used to analyse questionnaire responses. Results: There were a total of 257 respondents. Of these, 93% used medical photography, 70% used it to document a patient’s clinical progress, 70% to ask for a second opinion, 56% for education, 65% for research and publication, and 68% to present at medical conferences. Medical photography was used by 33% weekly and 36% monthly, with 71% of respondents always asking for the patients’ consent before taking a photograph. Doctors aged 20−40 years used photography more often than doctors over 40 years of age to document the clinical progress of the patients (77% and 52%, respectively, p = 0.01) and to ask for a second opinion (78% and 52%, respectively, p < 0.001). Conclusions: Our study shows that medical photography is a common practice amongst medical doctors. However, appropriate measures need to be created to obtain patients’ consent, store images, and sure the security of patients’ information.
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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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