Assessment of hair nickel and chromium levels in patients with a fixed orthodontic appliance: a systematic review and meta-analysis of case-control studies
Bibliographic record
Abstract
Introduction: The metals nickel (Ni) and chromium (Cr) can cause immunological sensitivity and adverse biological and cytotoxic effects.Aim: To evaluate hair levels of these metals in patients undergoing fixed orthodontic treatment compared with controls.Material and methods: Five databases -PubMed, Web of Science, Scopus, Cochrane Library, and ScienceDirectwere searched up to January 2018 for evaluation of the hair levels of nickel and/or chromium in patients undergoing fixed orthodontic treatment.To assess the study quality, the Newcastle-Ottawa Scale was used (NOS), and to compare hair Ni and Cr levels in the cases compared with the controls, a random-effects meta-analysis was performed by Review Manager 5.3 using standard mean differences (SMDs) and 95% confidence intervals (CIs).Results: Out of 38 studies in the databases searched, 6 studies were included in the meta-analysis.The pooled SMD of hair Ni levels between the cases and controls was 0.95 µg/g (95% CI: -0.09, 1.99; p = 0.07), which showed that the Ni level was similar in the cases compared with the controls, and that for hair Cr levels was 0.88 µg/g (95% CI: -0.45, 2.21; p = 0.20), so the Cr level was similar in the cases compared with the controls.Conclusions: The slightly elevated hair levels of Ni and Cr in patients undergoing fixed orthodontic treatment suggest that changing the components of fixed orthodontic appliances can be considered as an acceptable solution in the future.
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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 | 0.031 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.035 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".