Age-specific lip, oral cavity and pharynx cancer incidence rate in the world
Bibliographic record
Abstract
In order to make a comparison of the age-specific lip, oral cavity and pharynx cancer incidence rate between Japan and other countries, we abstracted cancer incidence rate from the Cancer Incidence in Five Continents Vol. XI (CI5) (1). The International Agency for Research on Cancer provides the CI5 databases on the incidence of cancer recorded by cancer registries (regional and national) worldwide. We used cancer incidence rate in five countries in Asia (China, India, Japan, Republic of Korea and Thailand), three countries in America (the USA, Canada and Brazil), two countries in Oceania (Australia and New Zealand) and four countries in Europe (the UK, France, Germany and Italy). Some countries have plural cancer registries and we aggregated the all registries to calculate the incidence rate in the countries from the CI5-XI database. The period of years at cancer diagnosis was from 2008 to 2012. Lip, oral cavity and pharynx cancer was coded as C00-C14 based on ICD-10. Age-specific lip, oral cavity and pharynx cancer incidence rate per 100 000 people in male. Age-specific lip, oral cavity and pharynx cancer incidence rate per 100 000 people in female. Figure 1 shows the age-specific incidence rates of lip, oral cavity and pharynx cancer by 5-year age groups for the selected countries in males. In general, incidence rates of lip, oral cavity and pharynx cancer in adults were on average higher in America and Oceania (except Australia) compared to countries in Asia and Europe, with the age-specific incidence rates showing a sharp increase until age 55–59 years, then started to slow down thereafter. In Asia, Thailand has seen a rapid increase after 40 years of age and showed the highest incidence rates in Asia, peaking at 55–59 years old (101.1 per 100 000). In America and Oceania, the USA showed highest incidence rates in older adults (109.2 per 100 000 in men aged 75–79 years), while Australia showed lower incidence rates after 50 years old than all other countries in this study. Europe also showed similar incidence curves, with the incidence rising according to age, and Germany showing the highest incidence rates in most of the age groups. Figure 2 shows the age-specific incidence rates of lip, oral cavity and pharynx cancer by 5-year age groups for the selected countries in females. Overall, women showed similar incidence rates as those of men until the age of 25–29 years across all regions, but the incidence rates of men started to surpass those of women after 30 years old. While we did not observe any cross-regional variations in age-specific incidence rates, within-regional differences were clearly observed. In Asia, India showed the lowest incidence rates in adults than all other countries in the region. In American and Oceania, New Zealand showed by far the highest incidence rates compared to all other countries in this study, peaking at 70–74 years old (43.7 per 100 000). In Europe, Germany again showed consistently highest incidence rates in the majority of the age groups than other countries in the region, which was similar to the incidence rates observed in males. Note: Data were downloaded from the Global Cancer Observatory (GCO), which is an interactive web-based platform presenting global cancer statistics (https://gco.iarc.fr/). Responsibility for this presentation and interpretation lies with the authors of this article.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".