Epidemiology of renal cancer in developing countries: Review of the literature
Bibliographic record
Abstract
INTRODUCTION: Renal cell carcinoma (RCC) is the ninth most common cancer in men, and the 14th most common cancer in women. It has been reported that the incidence of RCC is rising. These changes are more common in developed countries because of better screening programs and disease registry. The aim of this article is to review the epidemiology of RCC around the world. METHODS: A literature review of four databases was performed: PubMed, Embase, Lilacs, and Scielo. Studies of incidence, prevalence, mortality, and survival of RCC were taken from different countries. Studies included were published in the last 10 years. Two reviewers independently selected the studies. RESULTS: A total of 5275 references were reviewed by title and abstract. In the end, 42 references were selected for full-text review. The global incidence and prevalence of cancer vary. The highest incidence was described in North America and Northern Europe. In Canada, by 2007 the incidence was 17.9/100 000 and 10.3/100 000 in males and females, respectively. Developing countries like Colombia have fewer incidence rates, with less information in poor-income areas. CONCLUSIONS: We have seen a rise in the incidence and mortality of RCC globally. There is an association between RCC and smoking, obesity, hypertension, and socioeconomic status. Seeing the epidemiological data from some regions in developing countries and the lack of specialists in those places, it can be deduced there is underreporting of the disease that reveals the need to improve both surveillance and disease registration programs, especially in these countries.
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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.020 | 0.027 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".