Cancer studies based on secondary data analysis of the Taiwan's National Health Insurance Research Database
Bibliographic record
Abstract
There has been a surge in the academic publication output based on secondary analyses of the data from the Taiwan's National Health Insurance claim records. It has become a challenge to comprehend such a rapid expansion of the literature. Therefore, this study aimed to explore the conceptual content of National Health Insurance Research Database-based cancer research, using the abstract of articles extracted from PubMed between 2002 and 2015. Search terms including "National Health Insurance Research Database (NHIRD) AND Taiwan," "Taiwan AND population-based," and "Taiwan AND nationwide" were used to search in PubMed with the publication date limited to between 1997 and 2015. The retrieved articles were manually screened to retain only those that were cancer-related and were based on secondary data analysis of the NHIRD. A total 589 articles were selected for subsequent text mining using the R software. Among the 589 articles, the top 5 most studied cancer types were breast (16.3%), lung (11.4%), colorectal (10.4%), liver (8.3%), and prostate (7.5%). The articles that received the highest number of citations by PubMed Central articles were cited 92 times. The top 3 most frequently occurred keywords in the abstracts of the 589 articles were cancer, patient, and risk, with 3670, 2535, and 1652 times, respectively. Analysis of key conception indicated that the most common conceptions were diabetes, survival, breast cancer, lung cancer, and colorectal cancer. In conclusion, in this study of 589 published articles on secondary data analysis of the NHIRD, indexed by PubMed between 2002 and 2015, we found that while the risk factors of cancer, treatment of cancer, and survival of cancer patients were popular research topics, end-of-life cancer care issues were less studied. Further studies should explore these areas since they are as important as treatment of the disease itself for many patients.
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 | 0.037 | 0.149 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.058 | 0.050 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".