Reevaluation of Gastric Cancer Screening by Levin's Equation
Bibliographic record
Abstract
INTRODUCTION: This study aimed to reevaluate the effectiveness of fluoroscopy and endoscopy in reducing gastric cancer mortality at the population level. METHODS: Crude and age-adjusted mortality rates of gastric cancer and the introduction rates of gastric cancer screening were extracted from the Cancer Registry and Statistics database. The population-attributable risk (PAR) percent of no screening for gastric cancer mortality was calculated using Levin's equation. The PAR of each mortality rate in the no-screening group was estimated as follows: mortality × PAR%. The Jonckheere-Terpstra test for trends and linear regression were performed to compare the PAR of gastric cancer mortality rates among the decades. RESULTS: The PAR of crude and age-adjusted mortality rates in the no-screening group significantly decreased in the total population ( P for trend <0.001), as well as individually in the male ( P for trend <0.001) and female ( P for trend <0.001) populations. The PAR of the crude mortality rate in the female population significantly decreased in 2000-2009 and 2010-2019, compared with that in 1980-1989. There was no significant difference in the PAR of crude mortality rate in the male population among the decades. The PAR of the age-adjusted mortality rate significantly decreased in 2000-2009 and 2010-2019, compared with that in 1980-1989, in the male and female populations. DISCUSSION: PAR% and PAR of no screening for gastric cancer mortality could be estimated using Levin's equation, and the effectiveness of the present gastric cancer screenings with fluoroscopy and endoscopy has been decreasing, especially in the female population.
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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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".