Survival of teeth with external cervical resorption after Internal and External Repair: A Systematic Review
Bibliographic record
Abstract
Background: To analyze the survival rate of teeth affected by invasive cervical resorption after internal and external repair. Material and Methods: A search was conducted in PubMed/Medline, Web of Science, Embase, Scopus, the Cochrane Library, and gray literature at the DANS Easy Archive until September 2023. The selected studies were subjected to risk assessment of bias, and the quality of evidence was assessed using the Newcastle Ottawa Scale. The GRADE was used to analyze the certainty of evidence. Results: Three articles were included in this study. The Heithersay classification was used in all included studies. Only one study has reported on the Patel classification. Different results associated with the survival of treated invasive cervical resorption elements have been reported. Two studies reported a higher survival rate in externally repaired teeth than in internally repaired teeth. Only one study reported greater survival of theeth with external cervical resorption rate in the treatment with internal repair. The studies showed strong evidence and the certainty of the evidence was classified as very low. Conclusions: External cervical resorption, external repair, interal repair, survival rate, dental treatments.
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 | 0.006 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.012 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".