Public Skin Cancer Screenings with the Polka Dot Mama Melanoma Foundation
Bibliographic record
Abstract
Background: Skin cancer is a major public health burden. While treatable, early diagnosis usually portends a better prognosis. Screening is one preventive measure, although the United States Preventive Services Task Force has deemed there to be “insufficient evidence” for recommendation. The American Academy of Dermatology has sponsored free, public skin screenings since 1985, but there is minimal data supporting these efforts. The utility of community screening events in general is debated in the literature.Objective: To determine whether free, public skin cancer screenings are effective for secondary prevention of skin cancer through an analysis of follow-up rates of screening events hosted by the Polka Dot Mama Melanoma Foundation. To explore the challenges of implementing and measuring effectiveness of a community screening event.Methods: I conducted an observational study using survey data, along with a literature review on the value of community-oriented screening events.Results: Most screening participants were white, female, and 51 to 65 years old. More than half have had at least one blistering sunburn before age 20 years old and approximately half have used indoor tanning equipment. Over half of participants surveyed by telephone reported demonstrating sun-protective behaviors since their screening. Approximately one-third of participants had never seen a dermatologist prior to the screening. Diagnostic confirmation of four total skin cancers and two precancerous AKs appeared to result from these screening events.Conclusion: Community screenings are primarily helpful insofar as their success can be measured. It is important to study challenges associated with community screenings for the evaluation and success of future community screening events. The general sentiment toward skin cancer screenings is mixed, so future studies must focus on whether or not free skin screenings have a positive influence on the community and save lives.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".