Incidence trends in lung and bladder cancers in the Nordic Countries before and after the smoking epidemic
Bibliographic record
Abstract
Cigarette smoking epidemic, which started before the World War II, completely changed the cancer landscape. Reliable incidence data spanning the stepwise spreading epidemic are rare, but the Nordic cancer registries are unique sources in being able to catch the pre-epidemic situation in the female population where smoking became more prevalent after the War. For Swedish men, smoking prevalence has decease early and cancer rates may herald postsmoking rates. We used data from the NORDCAN database, constructed by the cancer registries of Denmark, Finland, Norway and Sweden, for the analysis of incidence changes in lung and bladder cancers from year 1943 (Denmark), from 1953 (Finland and Norway) and from 1960 (Sweden) until year 2016. The analyses revealed four novel observation relevant to the smoking epidemic. (1) The incidence of lung cancer in Norwegian women in the 1950s, when the smoking prevalence was very low, was 1.8/100 000 (world standard rate), which is at the level of lowest global female rates known to-date; (2) the earliest lung-to-bladder incidence ratio among Norwegian women was 0.64, probably benchmarking the incidence rates prior to the smoking epidemic; (3) bladder cancer incidence for Finnish women diagnosed in the 1950s was 1.2/100 000 which is at the level of the lowest rates currently known and (4) Swedish men with the lowest smoking prevalence in Europe, showed an epochal crossing of lung and bladder cancer incidence rates before year 2015. The data suggest that the approaching of the incidence rates for lung and bladder cancer can be expected in the course of the abating smoking epidemic.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".