MOBI-KIDS; STUDY ON COMMUNICATION TECHNOLOGY, ENVIRONMENT AND BRAIN TUMOURS IN YOUNG PEOPLE
Bibliographic record
Abstract
Background and Aims: Brain tumours are the second most common neoplasms in young people. Apart from exposure to ionizing radiation and genetic predispositions, little is known about risk factors for these tumours. Mobi-Kids will study effects of environmental factors on brain tumour risk in young people, with particular focus on electromagnetic fields from mobile phones and other communication technologies. Methods: Mobi-Kids is a collaborative case-control study starting in 12 countries (Austria, Australia, Canada, France, Germany, Greece, India, Israel, Italy, Spain, Taiwan, The Netherland)s using a common core protocol. Cases are 10-24 years old patients newly diagnosed with a brain tumour. Controls are patients hospitalized for appendicitis within the participating regions, individually matched to cases on age, sex and region of residence. A face-to-face interview will be held with participants and their parents to assess lifestyle, residential and school history, use of communication technologies, exposure to environmental risk factors, medical history and family history of cancer. Particular attention will be paid to issues of potential selection bias related to the low response rates of population-based controls (by selecting hospitalised controls) and potential recall errors (by validating questionnaire responses on mobile phone use with user data from network operators). Improved exposure indices for RF will be derived taking into account spatial distribution of energy in the brain at different ages; ELF from the phones will also be considered, as well as other important sources of EMF and chemical exposures in the general environment of young people. Results: Data collection has started in most participating countries.. The study is expected to include nearly 2000 brain tumour cases and twice as many controls during a 2.5-year study period. Conclusion: MOBI-KIDS will generate novel insights in brain tumour aetiology among young people. In particular it will address the association with information technology.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".