Role of community pharmacists in skin cancer screening: A descriptive study of skin cancer risk factors prevalence and photoprotection habits in Barcelona, Catalonia, Spain
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: Skin cancer incidence is increasing alarmingly, despite current efforts trying to improve its early detection. Community pharmacists have proven success in implementing screening protocols for a number of diseases because of their skills and easy access. OBJECTIVE: To evaluate the prevalence of skin cancer risk factors and the photoprotection habits with a questionnaire in community pharmacy users. METHODS: 2016. All participants received health education on photoprotection and skin cancer prevention. Patients with ≥1 skin cancer risk factor were referred to their physician, as they needed further screening of skin cancer. RESULTS: A total of 5,530 participants were evaluated. Of those, only 20.2% participants had received a total body skin examination for skin cancer screening in the past by a physician and 57.1% reported using a SPF 50+ sunscreen. 53.9% participants presented ≥1 skin cancer risk factor: 11.8% participants reported having skin cancer familial history and 6.2% reported skin cancer personal history; pharmacists found ≥10 melanocytic nevi in 43.8% participants and chronically sun-damaged skin in 21.4%. Lesions suspicious for melanoma were reported in 10.9% of the participants and urgent dermatological evaluation was recommended. CONCLUSIONS: Pharmacists can detect people with skin cancer risk factors amongst their users. This intervention can be considered in multidisciplinary strategies of skin cancer screening.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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