Crown dimensions of primary teeth—A systematic review and meta‐analysis
Bibliographic record
Abstract
Abstract Odontometrics, also known as metric traits, includes mesiodistal, buccolingual dimensions, and crown height. The purpose of this study was to assess pancontinental odontometric variations in the crown dimensions of primary teeth. Ten electronic databases were searched to identify studies that measured crown dimensions of primary teeth, published in English language, without year restriction up to July 2020. Studies included cross‐sectional research measuring on casts, subjects, and on radiographs of healthy children. Meta‐analysis was performed, and risk of bias was assessed using modified Newcastle–Ottawa Scale. Eighty‐seven observational studies were included, with 24,634 participants (9487 males, 11,083 females; 19 studies lacked gender information). Only one study showed a low bias risk, whereas 81 and 5 studies had moderate and high risk, respectively. Sixty‐five studies included for meta‐analysis revealed heterogeneity in mean mesiodistal dimensions of maxillary first molars from Asia (I2—99.7%), buccolingual measurements of mandibular first molars from Europe (I2—99.9%), crown height of mandibular second molars from Africa and Europe (I2—79.8%). Among mesiodistal and buccolingual dimensions, Australians have larger while Asians have smaller teeth. Pertaining to crown height, very few studies could be found in the literature. This review highlights the variations in crown dimensions of primary teeth among populations.
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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 | 0.010 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.021 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".