Sweet taste perception and dental caries experience among preschool children: a critical review
Bibliographic record
Abstract
Aim: The review aimed to analyze the relationship between sweet taste perception and dental caries among preschool children. Methodology: A literature search was conducted using PubMed, CINAHL, Dentistry, and Oral Sciences Source, and SCOPUS databases using the keywords “taste perception,” “sweet taste,” “dental caries,” and “dental decay.” The selection process involves two cycles. The inclusion criteria are documents that reported; sweet taste perception, dental caries experience, preschool children and written in English, and the exclusion criteria are; adults, review articles, letters to the editor, and case reports. The Newcastle Ottawa scale used for the quality analysis of the included studies. Results: 344 titles and abstracts were retrieved during the initial search. Upon screening and exclusion, only three articles were eligible for final analysis. The included studies were conducted in the United States of America, Brazil, and India, with sample sizes ranging from 38 to 191 children. Two studies were conducted in dental clinic settings, while one was in an educational center. Among the three studies, two studies achieved unsatisfactory scores, and one study with achieved a good score. Conclusions: Sweet taste perception and preference contribute to ECC. However, other important factors should be explored to employ various approaches to combat this disease.
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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".