Effectiveness evaluation of organized screening for esophageal cancer: a case-control study in Linzhou city, China
Bibliographic record
Abstract
In China, esophageal cancer has remained a large burden, and endoscopic screening is expected to reduce esophageal cancer mortality. Therefore, a population-based case-control study was conducted to evaluate the effect of screening. Cases were defined as individuals who had died of esophageal cancer, and controls were residents from the same area (three per case) who had not died of esophageal cancer, matched by gender and birth year. The exposure status (whether cases and controls had ever attended the screening or not) was acquired by inspecting the well documented screening records. A conditional logistic regression model was used to estimate the odds ratios (OR) and their 95% confidence intervals (95% CI). There were 253 cases and 759 controls. The reduction in risk of esophageal cancer mortality in individuals who had ever attended screening was 47% (OR: 0.53, 95% CI: 0.37-0.77). Compared with never-screened subjects, the ORs for screened subjects within 36 and 48 months before the reference date were 0.59(0.39-0.89) and 0.59(0.40-0.87); the ORs for 50-59 year old subjects were 0.48(0.28-0.85). The results suggest a 47% reduction in esophageal cancer mortality risk due to endoscopic screening, which may have significant implications for esophageal cancer screening in China, especially in rural areas.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".