Impact of COVID-19 pandemic on childhood cancer incidence and stage in France – A national registry-based study
Bibliographic record
Abstract
COVID-19 pandemic has considerably affected access to healthcare ressources with global decline in cancer care activities in 2020. This national study aimed to assess a possible impact of the pandemic on the management of pediatric cancer cases. Methods The study was based on the French National Childhood Cancer Registry and included all cases of cancer under 18 years of age diagnosed between January 1, 2016 and December 31, 2020. We estimated incidence rates, the proportion of advanced/metastatic stages (Toronto Pediatric Cancer Stage Guidelines) and the distribution of treatment initiation time during the 2020 pandemic period compared to the 2016-2019 reference period. Results Age-standardized incidence rates of overall pediatric cancer were similar in 2020 (161.4 cases per million person-years; 2,250 cases) and in 2016–2019 (162.4 cases per million person-years; 9,208 cases). They were also similar by sex, age group, region, and cancer type. We did not observe any significant differences in stage at diagnosis or median time to treatment. Conclusion Our nationwide population-based study suggests that pediatric cancer management was not substantially altered in 2020, despite the challenges induced by the COVID-19 pandemic and associated lockdown.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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".