Prevalence of mesio-distal dilaceration in patients presenting for initial orthodontic care: A retrospective study
Bibliographic record
Abstract
OBJECTIVE: To determine the prevalence of dilaceration in a sample of patients presenting for initial orthodontic care. METHODS: Examining radiographs from a random sample of orthopantomogram images was used to acquire the data. In all, 2,801 dental records were evaluated at Oman Dental College (ODC), Oman. A dental X-ray processing software was utilized to view the images. A tooth was classified as having a mesial/distal dilaceration if its long axis exhibited an angle of 90 degrees or greater. Dilacerated roots in the buccal/lingual direction were diagnosed by observing the appearance of a spherical opaque area with a dark shadow in its central region, projected by the apical foramen, which gave the root canal a "bull's-eye" appearance. RESULTS: Dilacerations were found in 17.32% of the records examined. The maxillary second molars (22.71%) were the most commonly affected, followed by the mandibular third molars and mandibular lateral incisors (21.90% and 17.23%, respectively). The central incisors and canines were the least affected, with dilaceration affecting less than 0.2% of the teeth. The mandible was found to have more dilacerations than the maxilla (53.78% and 46.22%, respectively). 61.03% of dilacerations occurred in molars, 43.12% of which occurred in third molars. CONCLUSION: Dilaceration is a notable dental anomaly that can affect any tooth, with some teeth being more affected than others. Dilaceration in maxillary second molars and mandibular lateral incisors is more common in the population of this study than in other populations reported in the literature. Recognizing the condition will allow for more effective orthodontic treatment.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".