Partitioning of time trends in prevalence and mortality of lung cancer
Bibliographic record
Abstract
BACKGROUND: Time trends of lung cancer prevalence and mortality are the result of three competing processes: changes in the incidence rate, stage-specific survival, and ascertainment at early stages. Improvements in these measures act concordantly to improve disease-related mortality, but push the prevalence rate in opposite directions making a qualitative interpretation difficult. The goal of this paper is to evaluate the relative contributions of these components to changes in lung cancer prevalence and mortality. METHODS: Partitioning of prevalence and mortality trends into their components using SEER data for 1973-2013. RESULTS: The prevalence of lung cancer increases for females and decreases for males. In 1998, the former was due to increased incidence (45%-50% of total trend), improved survival (40%-45%), and increased ascertainment at early stages (10%-15%). In males, a rapidly declining incidence rate overpowered the effects of survival and ascertainment resulting in an overall decrease in prevalence over time. Trends in lung cancer mortality are determined by incidence during 1993-2002 with noticeable contribution of survival after 2002. CONCLUSION: Lung cancer incidence was the main driving force behind trends in prevalence and mortality. Improved survival played essential role from 2000 onwards. Trends in stage ascertainment played a small but adverse role. Our results suggest that further improvement in lung cancer mortality can be achieved through advances in early stage ascertainment, especially for males, and that in spite of success in treatment, adenocarcinoma continues to exhibit adverse trends (especially in female incidence) and its role among other histology-specific lung cancers will increase in the near future.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 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".