Etiological Factors of the Midline Diastema in Children: A Systematic Review
Bibliographic record
Abstract
BACKGROUND: Midline diastema in children is a prevalent developmental entity, and this pathological condition may remain in many children due to various factors. Nonetheless, the evidence on etiological factors of the midline diastema in children is minimal. PURPOSE: To evaluate the etiological factors of midline diastema causes in children below 12 years of age from the published data. METHODS: covering the period from January 1960 to December 2019. Search in Google Scholar, grey literature, and hand search on references were performed to find additional data. Suitable studies were selected based on the predefined inclusion and exclusion criteria. Quality analysis of the chosen studies conducted using the Newcastle-Ottawa Scale (NOS) adapted for cross-sectional studies. RESULTS: Only eight studies were available for final analysis among those four studies from India, two studies from Korea, one study from Brazil, and another study from Canada. The most common etiology for midline diastema was supernumerary teeth followed by morphology labial frenum and nasal airflow condensation. The quality analysis of these studies based on NOS showed one study with unsatisfactory, four studies with satisfactory, and three with good quality. CONCLUSION: Morphology of frenum, pre-maxillary supernumerary teeth, and nasal airflow condensation seem to be the most common causes of midline diastema in children below 12 years.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".