Evaluating Outcomes and Prognostic Factors in Endodontic Microsurgery: A Retrospective Cohort Study
Bibliographic record
Abstract
INTRODUCTION: Outcome studies are essential for evidence-based endodontics. This study evaluates rates of radiographic periapical healing and survival to reintervention in endodontic microsurgery (EMS), along with the prognostic factors associated with these outcomes. METHODS: Dental records were reviewed to identify teeth treated with EMS at the University of Toronto between 1999 and 2021. Each tooth was tracked until the final follow-up (minimum of 1 year) or an untoward event. Radiographic periapical healing, survival, and their prognostic factors were analyzed using logistic and Cox regression, respectively. Median (interquartile range [IQR]), odds ratios (ORs), hazard ratios (HRs), and 95% confidence intervals (CIs) were calculated (P < .05). RESULTS: A total of 128 teeth met the criteria for radiographic periapical healing analysis, and 186 teeth were included in the survival analysis. The median follow-up time for radiographic periapical healing was 41.5 months (IQR: 27.8-72.9). At the latest follow-up, the overall radiographic periapical healing rate was 82.8%, and it was significantly associated with the absence of periodontal involvement (OR: 2.8, 95% CI: 1.1-7.7, P = .045) and root-end filling depth >2 mm (OR: 3.5, 95% CI: 1.2-9.6, P = .014). Median survival follow-up time was 37.5 months (IQR: 22.3-61.6); 86.6% of teeth survived without reintervention, 1.6% required treatment, and 11.8% were extracted. Age ≤45 (HR: 3.0, P = .036) and root-end filling ≤2 mm (HR: 4.3, P = .004) increased the risk of intervention. CONCLUSIONS: Both radiographic periapical healing and survival rates following EMS are high. Prognostic factors were identified, which require confirmation in future studies.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".