A Survey of Liver Cancer Specialists’ Views on the National Liver Cancer Screening Program in Korea
Bibliographic record
Abstract
Background/Aims: To reduce the cancer burden, the Korean government initiated the National Cancer Control Plan including the National Liver Cancer Screening Program (NLCSP). Ultrasonography examinations and α-fetoprotein tests at six-month intervals are currently offered for high-risk individuals. High-risk individuals are identified by reviewing the National Health Insurance Service claims data for medical use for the past two years using International Classification of Diseases Codes for specific liver disease. We surveyed the attitudes and opinions towards the NLCSP to understand the issues surrounding the NLCSP in Korea. Methods: Altogether, 90 Korean Liver Cancer Association members participated in online and offline surveys between November and December 2019. Results: Approximately one-quarter (27%) of the survey participants rated the NLCSP as very contributing and about two-thirds (68%) as contributing to some extent toward reducing hepatocellular carcinoma (HCC)-related deaths in Korea. Most (87.8%) responded that the current process of identifying high-risk individuals needs improvement. Many (78.9%) were concerned that the current process identifies individuals who use medical services and paradoxically misses those who do not. When asked for the foremost priority for improvement, solving 'duplication issues between the NLCSP and private clinic HCC screening practices' was the most commonly selected choice (23.3%). Conclusions: The survey participants positively rated the role of the NLCSP in reducing liver cancer deaths. However, many participants rated the NCLSP as needing improvement in all areas. This survey can be a relevant resource for future health policy decisions regarding the NLCSP in Korea.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".