Case: Control Matching Strategies on Associations between Cellular Telephone Use and Glioma Risk in the INTERPHONE Study
Bibliographic record
Abstract
Case: Control Matching Strategies on Associations between Cellular Telephone Use and Glioma Risk in the INTERPHONE StudyAbstract Number:1680 Michelle Turner*, Siegal Sadetzki, Chelsea Langer, Jordi Figuerola, Bruce Armstrong, Angela Chetrit, Graham Giles, Daniel Krewski, Martine Hours, Mary McBride, Marie-Elise Parent, Lesley Richardson, Jack Siemiatycki, Alistair Woodward, and Elisabeth Cardis Michelle Turner* Centre for Research in Environmental Epidemiology, Spain, E-mail Address: [email protected] , Siegal Sadetzki Chaim Sheba Medical Center, Israel, E-mail Address: [email protected] , Chelsea Langer Centre for Research in Environmental Epidemiology, Spain, E-mail Address: [email protected] , Jordi Figuerola Centre for Research in Environmental Epidemiology, Spain, E-mail Address: [email protected] , Bruce Armstrong The University of Sydney, Australia, E-mail Address: [email protected] , Angela Chetrit Chaim Sheba Medical Centre, Israel, E-mail Address: [email protected] , Graham Giles Cancer Council Victoria, Australia, E-mail Address: [email protected] , Daniel Krewski University of Ottawa, Canada, E-mail Address: [email protected] , Martine Hours Université de Lyon, France, E-mail Address: [email protected] , Mary McBride British Columbia Cancer Agency, Canada, E-mail Address: [email protected] , Marie-Elise Parent Universite du Quebec, Canada, E-mail Address: [email protected] , Lesley Richardson University of Montreal Hospital Research Centre, Canada, E-mail Address: [email protected] , Jack Siemiatycki University of Montreal Hospital Research Centre, Canada, E-mail Address: [email protected] , Alistair Woodward University of Auckland, New Zealand, E-mail Address: [email protected] , and Elisabeth Cardis Centre for Research in Environmental Epidemiology, Spain, E-mail Address: [email protected] AbstractAssociations between cellular telephone use and risk of brain tumors have been examined in a number of epidemiological studies including INTERPHONE. Although results revealed no positive associations between cellular telephone use and glioma risk overall, no exposure response, and no increased risk among long term users, there was some suggestion of an elevated risk among those in the highest decile of cumulative call time (odds ratio (OR) = 1.40, 95% confidence interval (CI) 1.03–1.89). However, there are potential methodological limitations including selection bias, reporting bias, and the fact that participant controls were interviewed later in time than cases during a time period of rapidly increasing cellular telephone use. Further work was conducted in a subset of five INTERPHONE study countries (Australia, Canada, France, Israel, New Zealand) using a post-hoc matching strategy optimizing case to control interview time to investigate the impact of matching and analytical strategy on associations between cellular telephone use and glioma risk with a particular focus on the distribution of time interval between interviews of case and control participants. In comparison with results based on the original INTERPHONE matching, analyses using closely post-hoc matched subjects in time (interviewed within 1 year) showed a small increased risk among long term users (OR 10+ years = 1.21, 95% CI 0.81-1.82) as well as a tendency towards increasing risk in the highest exposure categories, both for cumulative call time (OR 8-9 decile = 1.21, 95% CI 0.84-1.75; 10th decile = 1.47, 95% CI 0.93-2.33) and cumulative number of calls (OR 8-9 decile = 1.15, 95% CI 0.80-1.65; 10th decile = 1.23, 95% CI 0.79-1.93) in conditional logistic regression analyses. Matching time of case and control interviews may be an important factor to consider when exposure patterns are changing rapidly with time.
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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 | 0.019 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".