Overjet in Infants: A Cross-Sectional Study
Bibliographic record
Abstract
Objective The purpose of this study was to determine the normal ranges for overjet in healthy infants under 12 months of age. Design A cross sectional study of consecutive patients below 12 months of age. Setting The study was conducted at a private practice in Tampa, FL that specializes in pediatric craniomaxillofacial disorders. Patients All patients under the age 12 months were considered for entry into the study. Patients were excluded if they had temporomandibular joint pathology, sleep disordered breathing, facial trauma, or were diagnosed with a craniofacial anomaly. Interventions Measures of overjet, defined as the distance between the anterior surfaces of the alveolar ridges when in centric relation, were obtained. Main Outcome Measure The primary study outcome was the overjet of the enrolled patients. Results A total of 152 infants were included in this study. Of these, 51 were female, and 40 were born prematurely (ranging from 32–37 weeks of gestation). In neonates below 1 month of age, the mean overjet was 2.25 mm (95% CI 1.31–3.19). Multivariate linear regression analysis showed overjet to significantly decrease with age, at a mean rate of approximately 0.1 mm per month (coefficient of −0.09, 95% CI −1.61 to −0.02, p = 0.01). When controlling for potential confounders, average overjet was not shown to vary significantly between the sexes, with prematurity, with race, or with primary diagnosis at presentation. Conclusion This paper establishes normative values for overjet in infants below 12 months of age.
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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 | 0.003 |
| 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) | 0.001 | 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".