Worldwide Anatomic Characteristics of the Mandibular Canine—A Multicenter Cross-Sectional Study with Meta-Analysis
Bibliographic record
Abstract
INTRODUCTION: This study aimed to examine the global prevalence of root and root canal morphologies in mandibular canines and analyze potential influences of region, ethnicity, sex, and age on the proportion of a second root and root canal configuration. METHODS: Observers from 44 countries screened 13,200 canines using cone-beam computed tomographic exams and gathered data on the percentages of 2 root canal morphologies and 2-rooted configurations (primary outcomes), as well as the root canal configurations (secondary outcome). Demographic factors (ethnicity, sex, and age) were collected for each participant. Primary outcomes were represented as odds ratios and untransformed proportions accompanied by 95% confidence interval (CI) forest plots. Meta-analysis compared subgroups and identified sources of heterogeneity. Intra- and inter-rater tests were conducted. Statistical significance was set at 5%. RESULTS: The worldwide prevalence of a second canal was 7.5% (95% CI, 6.3%-8.7%), ranging from 0.7% in Nigeria to 17.7% in Uruguay. The meta-analysis also revealed significant variations when comparing ethnicity, gender, and age (P < .05). The global prevalence of a second root was 1.9% (95% CI, 1.5%-2.3%), with the highest proportion observed in Spain (6.7%). Caucasian and Indian (south Asian) ethnic groups, females, and older patients exhibited higher proportions of 2 roots (P < .05). Meta-regression excluded side, voxel size, and field of view as sources of heterogeneity (P > .05). CONCLUSIONS: The prevalence of 2 root canals and 2-rooted configurations in the mandibular canine exhibited variations based on geographic location, ethnicity, sex, and age. The global prevalence of 2 root canals and 2-root configuration was 7.5% and 1.9%, respectively.
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.014 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".