Global incidence, mortality and temporal trends of cancer in children: A joinpoint regression analysis
Bibliographic record
Abstract
Abstract Background/Methods The Cancer Incidence in Five Continents Time Trends, Nordic Cancer Registries, Surveillance, Epidemiology and End Results, WHO Mortality databases were assessed to extract the Age‐Standardised Rates (ASR) of cancer incidence and mortality among children aged 0–14 years old. By using the ASRs, the country‐specific Average Annual Percentage Change (AAPC) and its corresponding 95% confidence interval (CI) were calculated to determine the epidemiological cancer trend. Results In 2020, the highest incidence of childhood cancer was found in countries with higher Human Development Index (HDI) (ASR = 15.7), yet the highest mortality was found in countries with lower HDIs (ASR = 4.8). As for incidence, seven countries had positive AAPC among boys; Slovakia (AAPC2001–2010 = 4.98, 95% CI [1.66–8.40]), Ecuador (AAPC2003–2012 = 4.07, 95% CI [0.67–7.59]) and Thailand (AAPC2003–2012 = 3.69, 95% CI [0.37–7.11]) had the highest AAPC. Among girls, three countries had positive AAPC, which included Belarus (AAPC2003–2012 = 3.18, 95% CI [1.11, 5.29]), Canada (AAPC2003–2012 = 2.83, 95% CI [1.60, 4.07]) and Korea (AAPC2003–2012 = 1.76, 95% CI [0.23–3.32]). There was an overall decreasing trend of mortality. However, increased mortality was observed in two countries: Ecuador for boys (AAPC2007–2016 = 1.72, 95% CI [0.27–3.19]) and Austria for girls (AAPC2008–2017 = 4.11, 95% CI [0.38–7.98]). Conclusions The largest mortality and mortality to incidence ratio of childhood cancer were found in low‐income countries. There was a substantial increasing trend of childhood cancer incidence, while overall its mortality has been decreasing over the past decade. More studies are needed to confirm the drivers behind these epidemiologic trends.
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".