Evaluation of weather and environmental factors and their association with cutaneous melanoma incidence: A national ecological study
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: Cutaneous melanoma (CM) is a significant contributor to skin cancer-related mortality globally and in Canada. Despite the well-established link between ultraviolet (UV) radiation exposure and skin cancer risk, there remains a gap in population-level interventions and persistent misconceptions about sun exposure and impact of environment on individual behavior. Objective: The current study provides an ecological analysis using latest available data (2011-2017) to define geographic/environmental contributors to the CM landscape in Canada. Methods: Utilizing Canadian Cancer Registry and Canadian Urban Environmental Health Research Consortium data, we analyzed 39,605 CM cases occurring in Canada from 2011 to 2017. Environmental data, including UV radiation, greenspace (normalized difference vegetation index), temperature, heat events, and precipitation was used to evaluate the effect of environment on CM incidence rates across Forward Sortation Area postal codes. Results: Forward Sortation Areas with increased CM incidence were associated with higher annual average temperature, snowfall, heat events, normalized difference vegetation index, and vitamin D-weighted UV exposure. Conversely, factors associated with decreased incidence included an increased annual highest temperature, rain precipitation, and a longer duration of heat events. Limitations: This study is subject to ecological bias and findings should be interpreted with caution. Conclusion: This study further substantiates associations between specific environmental factors and CM incidence.
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 | 0.001 | 0.000 |
| 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) | 0.001 | 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