A Systematic Review on Water Fluoride Levels Causing Dental Fluorosis
Bibliographic record
Abstract
Dental fluorosis is a long-existing public health issue resulting from inequitable access to potable water. Socially disadvantaged rural communities in fluoride-endemic areas, where a conventional irrigation system is absent and groundwater containing natural fluoride is the predominant source of drinking water, face a significant oral public health threat. This study aimed to determine the association between water fluoride levels and dental fluorosis. A systematic review aligned with PRISMA principles was conducted using the SPIDER search methodology and relevant keywords on many search engines, such as Google Scholar, PubMed, Elsevier, Sage, Web of Science, Cochrane, and Scopus. This review sought to ascertain the PICO model’s application as a search strategy tool. The reviewers gathered and assessed 1164 papers from January 2010 to January 2023. In total, 24 research papers from diverse databases were included. Using the Newcastle–Ottawa Scale, grades resulting from several data screens were evaluated. According to a previous systematic review, there may be publication bias in studies examining the association between fluoride in drinking water and dental fluorosis. The findings of this systematic review indicate that subpar fluoride is detrimental to human health. The author outlines legislative tools and technological advancements that might reduce fluoride levels.
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 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 | 0.007 | 0.036 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".