Periapical Lesions and Missed Canals in Endodontically Treated Teeth: A Cone-Beam Computed Tomographic Study of a Chinese Subpopulation
Bibliographic record
Abstract
BACKGROUND Periapical lesions (PL) are a common complication of endodontically treated teeth (ETT), which can result from a missed canal (MC). This study aimed to assess the prevalence of PL and MC in the ETT of a Chinese subpopulation and investigate potential associations between them. MATERIAL AND METHODS A total of 561 cone-beam computed tomography images were selected and analyzed. A total of 1024 endodontically treated posterior teeth excluding third molars were evaluated for the presence of PL and MC. The chi-square test or Fisher's exact test, as well as the odds ratio test, were used to determine whether there was an association and risk relationship between the incidence of PL and the occurrence of MC. RESULTS The overall prevalence of PL and MC in ETT was 56.1% and 19.0%, respectively. In endodontically treated molars, the incidence of PL and MC was 64.1% and 27.6%, whereas in premolars, it was 42.1% and 4.27%. The maxillary first molar showed the highest frequency of PL (71.5%) and MC (65.7%), with the mesiobuccal second canal being the most missed (78.8%). Teeth with an MC were found to be 3.658 times (95% confidence interval=2.541-5.301, P<0.0001) more likely to be associated with a PL. CONCLUSIONS Endodontically treated teeth with missed canals are associated with higher risks of periapical lesions. The high prevalence of these complications in a Chinese subpopulation underscores the importance of implementing enhanced diagnostic and treatment methods for root canal treatment or retreatment.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".