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Record W2795483097 · doi:10.5114/ada.2018.74520

Assessment of hair nickel and chromium levels in patients with a fixed orthodontic appliance: a systematic review and meta-analysis of case-control studies

2018· review· en· W2795483097 on OpenAlexaboutno aff
Mohammad Moslem Imani, Mohadeseh Delavarian, Sepideh Arab, Masoud Sadeghi

Bibliographic record

VenueAdvances in Dermatology and Allergology · 2018
Typereview
Languageen
FieldMedicine
TopicContact Dermatitis and Allergies
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineMeta-analysisCochrane LibraryConfidence intervalChromiumDentistryWeb of scienceAdverse effectInternal medicineMetallurgy

Abstract

fetched live from OpenAlex

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 g/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 g/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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.811
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0130.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.052
GPT teacher head0.387
Teacher spread0.335 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designMeta-analysis
Domainnot available
GenreReview

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".

Quick stats

Citations5
Published2018
Admission routes1
Has abstractyes

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