Prevalence of the Cusp of Carabelli: a systematic review and meta-analysis
Bibliographic record
Abstract
The Cusp of Carabelli (CoC) is the most commonly occurring dental morphological trait. To provide a pancontinental overview on the prevalence of the CoC in primary maxillary second molars and permanent maxillary molars. An electronic search was conducted on ten databases without year restrictions up to July 2020. All cross-sectional studies published in the English language reporting prevalence estimate of CoC were included. A modified version of the Newcastle–Ottawa scale was used to assess study quality. Meta-analyses were conducted for studies that reported data using Dahlberg and ASUDAS classification across continents. For qualitative synthesis, 142 studies (45,327 participants) were included, of which 130 studies had moderate risk of bias. Random effects meta-analysis was performed for 41 studies. For prevalence of CoC in primary maxillary second molars, the estimate was 72% (2,829 participants). The overall percentage attained for permanent maxillary molars was 59% (16,607 participants) for first molars; 8% (2,277 participants) for second molars; and 10% (89 participants) for third molars. Subgroup analysis revealed the European continent reported the highest prevalence in permanent maxillary first and second molars. Primary maxillary second molars recorded highest prevalence of CoC followed by permanent maxillary first, third and second molars. Pancontinental studies with regard to primary maxillary second molars are warranted.
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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 | 0.013 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.027 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".