Bibliographic record
Abstract
Cancer incidence and mortality trends in the Nordic countries show that most cancer types have been increasing for a long time, while a few show decreasing trends. The object of this study was to investigate melanoma mortality trends to see if there is a specific year for the trend breaks, possibly indicating a common causing factor affecting most of the population from the same time. The results clearly show that melanoma mortality started to increase exponentially by the time lived as an adult since 1955 and that the trends easily can be modeled and used for projection purpose. The findings are in support of earlier studies, suggesting reduced or temporarily disturbed DNA repair capacity due to a population-wide environmental change to be the main cause to increasing cancer rates in general, and increasing melanoma incidence and mortality in particular. th century. Traditionally, increased sun tanning habits have been blamed as the main cause of this public health problem. However, the mortality has stabilized among younger age groups while continued to increase in an exponential way among the older groups. This fact suggests that the melanoma risk suddenly increased for the whole population and that those younger age groups, having lived all their lives in this new environment, then should be expected to show a mortality level stabilizing at a higher level. In order to better understand the trends noticed, we wanted to make a trend model considering a specific year for the start of the mortality increase, the exponential function used and the age at which the mortality increase takes action. METHODS We conducted a detailed analysis of age-specific melanoma mortality rates from 1952 to the present in order to investigate if a specific trend break year was associated with each age-specific trend line. We further investigated whether such trend break years would identify a specific year from which the whole population of Sweden might have been exposed to new environmental factors that may have increased or decreased cancer rates over time.
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.
How this classification was reachedexpand
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 | 0.002 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".