Trends of cervical cancer in Greenland: A 60-year overview
Bibliographic record
Abstract
Background. Due to its extraordinarily fast economic and social transition, virtually closed borders before 1940 and, moreover, that 85% of the population has the distinctive genetics of the Inuit, Greenland is a very interesting country to study cervical cancer from a historical perspective. Nevertheless, little has been reported about long-term cancer trends in Greenland. Our aim was to describe and interpret the incidence of cervical cancer from 1950 to 2009. Material and methods. We systematically searched PubMed for articles reporting the incidence of cervical cancer in Greenland. We supplemented this with data for 1980–2009 obtained from the Chief Medical Officer of Greenland. Results. Incidence of cervical cancer was around 10 per 100 000 women (age-standardised, world population, ASW) in the 1950s, 30 per 100 000 in the 1960s, and in the 1980s around 60 per 100 000. From 1985 onwards, the incidence of cervical cancer started decreasing to the current level of 25 per 100 000. Conclusion. The steep increase in the incidence of cervical cancer from the 1950s onwards is unlikely to be explained by increasing completeness of data. In parallel with the economic development, however, out-of-wedlock births (proxy for sexual behaviour) increased dramatically from 1935 onwards while tobacco use increased from the 1950s onwards. From the late 1960s to around 1990, data suggested rather stable but high levels of sexual habits. The decrease in the incidence of cervical cancer since 1985 is consistent with the introduction of screening. The data strongly suggested that the increased burden of cervical cancer in Greenlandic women was real and followed earlier changes in sexual behaviour; these changes were likely a consequence of the tremendous societal changes.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.020 | 0.023 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".