Knowledge of molar incisor hypomineralization among physicians and dentists
Bibliographic record
Abstract
Background: Molar incisor hypomineralization (MIH) is a global dental condition. Early intake of antibiotics can increase the risk of MIH. Purpose: To assess the knowledge of physicians and dentists on MIH and its associations with antibiotics exposure during early childhood. Methods: This cross-sectional study surveyed the above health professionals utilizing an electronically distributed questionnaire. The chi-square test was used to compare differences in levels of knowledge between the study groups. Results: There were 335 participants in the study; general physicians (n=79), pediatricians (n=98) and dentists (n=158). A significantly lower proportion of general physicians and pediatricians had knowledge of MIH compared to dentists (19% and 18% vs. 82%, respectively, P<0.001). There was no statistically significant difference between all groups regarding their knowledge about the association between antibiotics prescribed during the first four years of life and MIH development (P=0.07). Conclusions: Physicians and pediatricians lacked knowledge about the dental condition of MIH. Most study respondents did not know the association between frequent antibiotic intake during early childhood and the development of MIH. Since medical practitioners are more likely to prescribe antibiotics and have a greater impact on early childhood health, raising awareness of MIH and its relationship with antibiotic exposure in early life among medical practitioners is essential.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".