Prevalence and Clinical Characteristics of Teeth Extracted with a Diagnosis of Cracked Tooth: A Retrospective Study
Bibliographic record
Abstract
The body of knowledge that exists regarding cracked teeth is limited. The purpose of this study was to determine the prevalence of cracks among extracted teeth. This retrospective longitudinal cohort study included patients of the Virginia Commonwealth University School of Dentistry that underwent extraction procedures over a 6 year period. The sample consisted of 20,408 patients and 40,870 teeth. Statistical analysis software was used to identify diagnoses of a crack, fracture, or split tooth prior to extraction of the tooth by analyzing the Electronic Health Record (EHR) (axiUm™, Version 6.03.03.1035, Exan Corporation, Vancouver, BC, Canada). There were 3,228 teeth identified as cracked in the 40,870 extracted teeth—an overall prevalence of 7.90%. The percentage of cracked teeth were compared using a chi-square test of homogeneity. The prevalence of cracked teeth varied according to tooth type (chi-square = 95.5, df = 7, p < .0001). Tukey’s multiple-comparison procedure identified the groups of tooth types with a significantly different cracked prevalence. The mandibular 2nd molar had the highest prevalence (9.72%). Age and gender were also significantly correlated with cracked teeth.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| 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".