Association Between the Presence of Missed Canals, Detected Using CBCT, and Post-Treatment Apical Periodontitis in Root-Filled Teeth: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background. Post-treatment apical periodontitis (PAP) is a frequent consequence of root canal treatment (RCT) failure, often caused by untreated missed canals in root-filled teeth (RFT). While cone-beam computed tomography (CBCT) has been used to find these missed canals, the results are controversial. This systematic review and meta-analysis investigates the association between PAP in RFT and missed canals detected via CBCT. Methods. Two independent reviewers searched PubMed, Scopus, Dialnet, and SciELO for relevant articles published up until 17 February 2025. The main outcome was the prevalence of PAP in RFT with and without missed canals detected via CBCT. The overall odds ratio (OR) was calculated using a binary random effects model meta-analysis (OpenMeta Analyst). Risk of bias was assessed using the Newcastle–Ottawa Scale, and certainty was evaluated using GRADE. Results. Eight cross-sectional studies (9983 RFT) were included in the review. The pooled prevalence of PAP was significantly higher in RFT with missed canals (85.1%) than those without (56.3%). The meta-analysis showed a strong association between missed canals and PAP (OR = 7.17, 95% CI = 4.55–11.29), indicating a sevenfold increased likelihood. Maxillary molars, especially first molars, most commonly had missed canals. Heterogeneity was high (I2 = 86%), and evidence certainty was low, due to methodological limitations. Conclusions. Untreated missed canals significantly increase the likelihood of PAP in RFT, highlighting the need for thorough canal detection and treatment. Clinicians should prioritize anatomical knowledge and advanced imaging to minimize treatment failure.
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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 | 0.012 | 0.032 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.038 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".