Current Trends of Lung Cancer Surgery and Demographic and Social Factors Related to Changes in the Trends of Lung Cancer Surgery: An Analysis of the National Database from 2010 to 2014
Bibliographic record
Abstract
PURPOSE: We investigated current trends in lung cancer surgery and identified demographic and social factors related to changes in these trends. MATERIALS AND METHODS: We estimated the incidence of lung cancer surgery using a procedure code-based approach provided by the Health Insurance Review and Assessment Service (http://opendata.hira.or.kr). The population data were obtained every year from 2010 to 2014 from the Korean Statistical Information Service (http://kosis.kr/). The annual percent change (APC) and statistical significance were calculated using the Joinpoint software. RESULTS: From January 2010 to December 2014, 25,687 patients underwent 25,921 lung cancer surgeries, which increased by 45.1% from 2010 to 2014. The crude incidence rate of lung cancer surgery in each year increased significantly (APC, 9.5; p < 0.05). The male-to-female ratio decreased from 2.1 to 1.6 (APC, -6.3; p < 0.05). The incidence increased in the age group of ≥ 70 years for both sexes (male: APC, 3.7; p < 0.05; female: APC, 5.96; p < 0.05). Furthermore, the proportion of female patients aged ≥ 65 years increased (APC, 7.2; p < 0.05), while that of male patients aged < 65 years decreased (APC, -3.9; p < 0.05). The proportions of segmentectomies (APC, 17.8; p < 0.05) and lobectomies (APC, 7.5; p < 0.05) increased, while the proportion of pneumonectomies decreased (APC, -6.3; p < 0.05). Finally, the proportion of patients undergoing surgery in Seoul increased (APC, 1.1; p < 0.05), while the proportion in other areas decreased (APC, -1.5; p < 0.05). CONCLUSION: An increase in the use of lung cancer surgery in elderly patients and female patients, and a decrease in the proportion of patients requiring extensive pulmonary resection were identified. Furthermore, centralization of lung cancer surgery was noted.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.000 | 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".