Profiles and Findings of Population-Based Esophageal Cancer Screening With Endoscopy in China: Systematic Review and Meta-analysis
Bibliographic record
Abstract
BACKGROUND: Population-based esophageal cancer (EC) screening trials and programs have been conducted in China for decades; however, screening strategies have been adopted in different regions and screening profiles are unclear. OBJECTIVE: We performed a meta-analysis to profile EC screening in China by positivity rate, compliance rate, and endoscopy findings, aiming to provide explicit evidence and recommendations for EC screening programs. METHODS: English (PubMed, Embase) and Chinese (China National Knowledge Infrastructure, Wanfang) language databases were systematically searched for population-based EC screening studies in the Chinese population until December 31, 2022. A meta-analysis was performed by standard methodology using a random-effects model. Pooled prevalence rates were calculated for three groups: high-risk areas with a universal endoscopy strategy, rural China with a risk-stratified endoscopic screening (RSES) strategy, and urban China with an RSES strategy. Positive cases included lesions of severe dysplasia, carcinoma in situ, intramucosal carcinoma, submucosal carcinoma, and invasive carcinoma. RESULTS: The pooled positivity rate of the high-risk population was higher in rural China (44.12%) than in urban China (23.11%). The compliance rate of endoscopic examinations was the highest in rural China (52.40%), followed by high-risk areas (50.11%), and was the lowest in urban China (23.67%). The pooled detection rate of positive cases decreased from 1.03% (95% CI 0.82%-1.30%) in high-risk areas to 0.48% (95% CI 0.25%-0.93%) in rural China and 0.12% (95% CI 0.07%-0.21%) in urban China. The pooled detection rate of low-grade intraepithelial neoplasia (LGIN) was also in the same order, being the highest in high-risk areas (3.99%, 95% CI 2.78%-5.69%), followed by rural China (2.55%, 95% CI 1.03%-6.19%) and urban China (0.34%, 95% CI 0.14%-0.81%). Higher detection rates of positive cases and LGIN were observed among males than among females and at older ages. The pooled early detection rate was 81.90% (95% CI 75.58%-86.88%), which was similar to the rates in high-risk areas (82.09%), in rural China (80.76%), and in urban China (80.08%). CONCLUSIONS: Under the current screening framework, a higher screening benefit was observed in high-risk areas than in other regions. To promote EC screening and reduce the current inequality of screening in China, more focus should be given to optimizing strategies of high-risk individual assessment and surveillance management to improve compliance with endoscopic examination. TRIAL REGISTRATION: PROSPERO CRD42022375720; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=375720.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.017 | 0.035 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.039 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".