Incidence of gastrointestinal stromal tumor in Chinese urban population: A national population‐based study
Bibliographic record
Abstract
BACKGROUND: Information on incidence of gastrointestinal stromal tumor (GIST), the most common type of mesenchymal tumor in gastrointestinal tract, was limited in China. This study aimed to estimate the incidence of GIST in urban population from mainland China in 2016. METHODS: Urban Employee Basic Medical Insurance (UEBMI) and Urban Residence Basic Medical Insurance (URBMI) in China were used. The denominator of incidence was the total person-years of insured individuals in 2016 in the database, covering approximately 0.43 billion individuals. The numerator was the number of incident GIST cases in 2016. RESULTS: The crude incidence in 2016 was 0.40 per 100,000 person-years (95% CI, 0.06-1.03). Male incidence was higher than female incidence (0.44 vs. 0.36, rate ratio: 1.22, p < 0.001). The mean age at diagnosis was 55.20 years (SD = 14.26) and the incidence among those aged 50 years or older was 2.63 times (0.84 vs. 0.32, p < 0.001) higher than those aged under 50. The highest incidence was observed in East China (2.29, 95% CI: 0.46-5.54). CONCLUSIONS: The incidence of GIST in mainland China was lower than Europe, North America and Korea. The mean age at diagnosis of GIST in China was younger than that of Europe and Canada. This study provides useful information to further research, policy formulating and management of GIST.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".