International variations in soft tissue sarcoma incidence in children and adolescents
Bibliographic record
Abstract
In order to compare the subtype distribution of soft tissue sarcoma in children and adolescents between Japan and other countries, we extracted information on cancer incidence in children and adolescents from the third volume of the International Incidence of Childhood Cancer series (IICC-3) (1). The IICC-3 reports the number or incidence rates of cancers diagnosed in childhood and adolescence, from cancer registries (regional or national) worldwide. We analysed soft tissue sarcoma incidence in four countries in Asia (Japan, China, the Republic of Korea and Thailand), two countries in Africa (Egypt and Uganda), four countries in the Americas (North: The United States of America and Canada, Latin and Caribbean: Brazil and Colombia), three countries in Europe (the United Kingdom [UK], France and Germany) and two countries in Oceania (Australia and New Zealand). Information from the Republic of Korea, USA, UK, Australia and New Zealand was obtained at the national level, and that from the other countries was extracted from one or multiple regional cancer registries. The years of incidence included in the analyses varied from country to country, ranging from 1990 to 2014, with the shortest being 12 years (Egypt: 1999–2010, UK: 2000–2011) and the longest being 24 years (Japan and China: both 1990–2013). In this study, we compared the incidence and proportional distribution of soft tissue sarcoma subtypes in children (0–14 years old) and adolescents (15–19 years old) between these countries. As shown in Table 1, with the exception of Uganda, the incidence rates of soft tissue sarcomas varied between 5.0 and 10.3 per 1 000 000 person-year in children (0–14 years old) and between 5.5 and 11.5 per 1 000 000 persons-year in adolescents (15–19 years old), with rhabdomyosarcomas as the leading subtype for most countries. In contrast, in Uganda, due to higher incidence of Kaposi sarcoma, sarcoma incidence rates were notably higher than other countries, respectively, representing 36.2 and 38.2 per 1 000 000 persons-year in children and adolescents. For all countries, incidence rates were consistently higher for adolescents than for children in all countries. Both for children and adolescents, the incidence rate was lowest in Asia, followed in turn by Europe and America (Latin and Caribbean), Oceania and America (North). Incidence rates were outlying in Africa, with relatively low rates in Egypt, and highest rates in Uganda in comparison to all other countries (up to 7-fold of lowest incidence rate). Incidence rates of soft tissue sarcoma in children and adolescents (per 1 000 000 person-years) aAge-standardized incidence rate. Figure 1 shows the proportional distribution of subtypes of soft tissue sarcoma incidence in children aged 0–14 years by country. Except for Uganda, the most frequently identified subtypes were rhabdomyosarcoma (with proportions ranging from 41.2% in China to 60.3% in Brazil), followed by fibrosarcoma (with proportions ranging from 5.9% in France to 13.6% in China). Other specified ranged from 21.9% for Brazil to 36.3% for Egypt. Uganda was an exception for all subtypes as the most frequent subtype was Kaposi sarcoma (proportion of 78.6%, by contrast with all other countries, for which the proportion ranged from 0.0% for most countries and up to 0.6% for the remaining ones), followed by rhabdomyosarcoma (13.5%) and fibrosarcoma (2.2%). Proportional distribution of subtype of soft tissue sarcoma in children (0–14 years old). Figure 2 shows the proportional distribution of subtypes of soft tissue sarcoma in adolescents aged 15–19 years. Proportions of subtypes showed highest differences in rhabdomyosarcoma and ‘other specified’, with an average of decrease by half for rhabdomyosarcomas (range of 11.8% for Uganda to 29.3% in New Zealand, including a most prominent decrease for France, from 56.4% for children to 16.5% for adolescents). In line with the decrease of rhabdomyosarcoma, ‘other specified’ turned into the most prominent subtype, again apart from Uganda, ranging from 36.6% for Brazil to 60.7% in Egypt. The proportion of Kaposi sarcoma in adolescents remained similar to that of for children (70.9% in Uganda and below 4.6% for all other countries). Proportional distribution of subtype of soft tissue sarcoma in adolescents (15–19 years old). Note: Data were extracted from the third volume of the International Incidence of Childhood Cancer series (IICC-3) (1). The table and figures are prepared by the authors of this article, and the responsibility for this presentation and its interpretation lies with the authors of this article.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".