Global incidence, mortality and temporal trends of cancer in children: A joinpoint regression analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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 (AAPC 2001–2010 = 4.98, 95% CI [1.66–8.40]), Ecuador (AAPC 2003–2012 = 4.07, 95% CI [0.67–7.59]) and Thailand (AAPC 2003–2012 = 3.69, 95% CI [0.37–7.11]) had the highest AAPC. Among girls, three countries had positive AAPC, which included Belarus (AAPC 2003–2012 = 3.18, 95% CI [1.11, 5.29]), Canada (AAPC 2003–2012 = 2.83, 95% CI [1.60, 4.07]) and Korea (AAPC 2003–2012 = 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 (AAPC 2007–2016 = 1.72, 95% CI [0.27–3.19]) and Austria for girls (AAPC 2008–2017 = 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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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 | 0.004 |
| 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) | 0.004 | 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 it