Effectiveness of Risk-Adapted Upper Gastrointestinal Cancer Screening in China: Prospective Cohort Study
Bibliographic record
Abstract
Background: Previous studies have proved the effectiveness of endoscopic screening in rural areas; however, long-term, high-quality evidence regarding the effectiveness of risk-adapted upper gastrointestinal cancer (UGC) sequential screening strategies in resource-rich regions is currently lacking. Objective: The objectives were to validate the effectiveness of risk-adapted sequential screening strategies in UGC prevention and control and assess the potential of sequential screening to lower mortality rates. Methods: Based on the Cancer Screening Program in Urban China, a prospective, large-scale cohort study based on population was conducted to recruit individuals from 4 cities in China from 2013-2019. Those identified as having a high risk of UGC according to a validated risk-score model were advised to undergo endoscopy tests. Follow-up outcomes were tracked until June 2021. Incidence of UGC, UGC-related mortality, and all-cause mortality were evaluated between the screened and nonscreened cohorts. Results: The study included 153,079 participants at baseline. In total, 113,916 (74.42%) of the participants were designated as low risk of UGC. The remaining 39,163 (25.68%) participants were deemed to be at high risk of UGC and were offered gastroscopy tests. Among the high-risk participants, 9627 (compliance rate 24.6%) adhered to the gastroscopy tests. Over a median follow-up of 6.05 (IQR 3.06-7.06) years, 622 UGC cases, 180 UGC deaths, and 1958 all-cause death cases were traced. The screened cohort exhibited the highest cumulative incidence of UGC (119.2 per 100,000 person-years), followed by the nonscreened and low-risk cohorts. Obvious reductions in both all-cause mortality and UGC mortality were observed between those who undertook screening (153.7 and 4.7 per 100,000 person-years, respectively) and the nonscreened group (245.3 and 27 per 100,000 person-years, respectively). The screening population showed a significant 36% and 82% reduction in both all-cause mortality (hazard ratio [HR] 0.64, 95% CI 0.49-0.83, P<.001) and UGC mortality (HR 0.18, 95% CI 0.04-0.74, P=.02), respectively, compared to the nonscreened group. Reductions of 35% in all-cause mortality (HR 0.65, 95% CI 0.49-0.86, P=.003) and 81% in UGC mortality (HR 0.19, 95% CI 0.05-0.80, P=.02) were observed in participants aged older than 55 years in the screened group compared to the nonscreened group. The reductions in all-cause mortality and UGC mortality were statistically significant in males (all-cause mortality: HR 0.64, 95% CI 0.47-0.88, P=.005; UGC mortality: HR 0.10, 95% CI 0.01-0.72, P=.02), but significant reductions were not observed in females (all P values were >.05). Conclusions: Our study suggests the significance of one-off risk-adapted UGC screening in reducing both all-cause mortality and UGC mortality, particularly among high-risk individuals, indicating its effectiveness in UGC prevention and management.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".