Human Premolar Crown Sizes are Highly Variable Even in the Same Mouth, with Strong Crown Size Correlations among Premolars and All Molars Except the Third Molar
Bibliographic record
Abstract
The Inhibitory Cascade Model (ICM) predicts in mice that a larger permanent first molar (M1) results in a smaller second molar (M2) and an even smaller third molar (M3). Counter to the ICM, our lab found in contemporary humans that instead of M1 size, only M2 size predicts M3 size. Our lab also identified in humans 13 different molar size ratio patterns instead of one classic ICM pattern (M1>M2>M3). Further, other work in nonhuman primates reported a Premolar Molar Module (PMM) where sizes of the fourth premolar (P4) and all three molars are highly correlated. However, premolar:molar size correlation in contemporary humans is yet to be explored. This exploration can help define how the development of these two different classes of teeth influence each other’s sizes. In humans, M1 initiates near birth, followed ~24 months later by P3 and P4 (almost simultaneously with each other), then ~12 months later by M2, and ~6 years later by M3. Our first aim was to define variation in premolar size ratio pattern. We hypothesized that all three mathematically possible premolar size ratio patterns (P3=P4, P3>P4, P3 M2>M3). We hypothesized that absolute sizes as well as size ratios of earlier initiating teeth will predict the absolute sizes and size ratios of later initiating teeth. Maximum mesiodistal lengths of P3, P4, M1, M2, and M3 crowns were measured in millimeters to represent absolute tooth sizes using retrospective Cone Beam Computed Tomography scans of 91 dental patients (54 female, 37 male) aged 13‐23 years. A frequency analysis revealed all three mathematically possible premolar size ratio patterns in our sample (P3>P4: 49.83%, P3
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".