External cervical resorption – Treatment outcomes and determinants: A retrospective cohort study with up to 10 years of follow‐up
Bibliographic record
Abstract
AIM: To assess long-term external cervical resorption (ECR) treatment outcomes in relation to both local and treatment-related determinants. METHODOLOGY: Information was available for 76 patients (98 teeth) who were diagnosed with ECR during the period from 2008 to 2018 at the University of British Columbia graduate endodontics clinic. The ECR patients were followed up, and a clinical and radiographic examination was conducted. Chi-square test compared failure rates amongst different subgroups. The survival analysis was used to evaluate the overall ECR survival/failure rates in relation to several local and treatment-related determinants. RESULTS: Overall, 67 patients (89 teeth) were followed up. The mean follow-up time was 3.9 years, and the minimum was 1 year. Twenty-four teeth failed (19 extracted, 5 not functional), and the overall probability of failure was 50.0% 8 years after the diagnosis. Significant (p < .05) local ECR determinants were tooth location and the Heithersay classification, and treatment-related determinants were root canal treatment (RCT) and the ECR repair combined with RCT. Treatment outcomes for Heithersay class 1 and 2 cases were better than for class 3 and 4 cases. CONCLUSIONS: Higher failure rates were associated with posterior tooth location and higher Heithersay class, whilst RCT and ECR repair combined with RCT were associated with lower failure rates.
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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) | 0.000 | 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 | 0.001 |
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".