A meta-analysis of the risk of salivary gland tumors associated with mobile phone use: the importance of correct exposure assessment
Bibliographic record
Abstract
OBJECTIVES: To investigate the risk of developing salivary gland tumors associated with the use of mobile phones. CONTENT: There have been a number of epidemiological studies conducted to assess for a possible association between mobile phone usage and the development of intracranial tumours, however results have been conflicting. We conducted an extensive literature search across four different databases was conducted. After selecting the articles relevant to the area of study, a total of seven studies were included in this meta-analysis, with no restrictions set on publication date or language. Studies were qualitatively assessed using the Newcastle-Ottawa scale. No significant association between the use of mobile phones and salivary gland tumors was observed (OR=1.06, 95% CI=0.86-1.32). No evidence for publication bias was detected. SUMMARY AND OUTLOOK: Our findings indicate no significant association between mobile phone usage and salivary gland tumours. However, there were many limitations encountered in these studies, suggesting that the observed result may not be an accurate estimate of the true carcinogenic risk of mobile phones, especially for heavy long-term users. In fact, the studies included in this meta-analysis highlight the need to correctly define exposure assessment in order to ascertain the risk of a certain variable.
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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 | 0.014 | 0.030 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.032 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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 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".