Temporal clustering of neuroblastic tumours in children and young adults from Ontario, Canada
Bibliographic record
Abstract
BACKGROUND: The aetiology of neuroblastic tumours is likely to involve both genetic and environmental factors. A number of possible environmental risk factors have been suggested, including infection. If an irregular temporal pattern in incidence is found, this might suggest that a transient agent, such as an infection, is implicated. Previous work has found evidence for temporal clustering in children and young adults living in northern England. METHODS: We examined data from a second population-based registry from Ontario, Canada to determine whether there was evidence of temporal clustering of neuroblastic tumours. Cases diagnosed in children and young adults aged 0-19 years between 1985 and 2016 were extracted from the population-based Pediatric Oncology Group of Ontario Networked Information System (POGONIS). A modified version of the Potthoff-Whittinghill method was used to test for temporal clustering. Estimates of extra-Poisson variation (EPV) and standard errors (SE) were obtained. RESULTS: Eight hundred seventy-six cases of neuroblastic tumours were diagnosed during the study period. Overall, no evidence of temporal clustering was found between fortnights, between months or between quarters within years. However, significant EPV was found between years within the full study period (EPV = 1.05, SE = 0.25; P = 0.005). CONCLUSIONS: The findings are consistent with the possibility that a transient agent, such as an infection that is characterised by 'peaks and troughs' in its occurrence, might be implicated in the aetiology of neuroblastic tumours. However, this pattern may also reflect a long-term increase in the numbers of cases, rather than peaks and troughs.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| 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".