The Outcomes of Endodontically Treated Teeth Restored with Custom-made Cast Post-and-core Restorations: A Retrospective Cohort Study
Bibliographic record
Abstract
Introduction: Custom-made cast post-and-core (CMCPC) restorations have long been used to restore structurally deficient endodontically treated teeth (ETT). However, the evidence regarding their impact on the outcomes of ETT is largely inconclusive. This study evaluated the long-term treatment outcome of ETT restored with CMCPC. Methods: This retrospective cohort study examined the dental records of patients with CMCPC placed at a specialty private practice in Toronto, Canada, between 1999 and 2021. The proportion of ETT with complete periapical healing and those that survived were estimated, and prognostic factors were investigated using multiple logistic and Cox regression analyses, respectively (P<.05). Results: A total of 500 and 1000 teeth met periapical healing and survival criteria, respectively. The periapical healing rate was 88.8% and was associated with the presence of baseline periapical radiolucency [OR=0.1, 95% Confidence Intervals (CI):0.05–0.2; P<.001]. The survival, after a mean follow-up time of 68.9±54.07 months (range:0.5–251.9), was 87.9% and was associated with <75% of root length in bone [Hazard Ratio (HR) =2.6; 95%CI:1.0–6.6; P=.033], type and quality of final restoration [HR=2.09; 95%CI:1.1–3.9; P=.020; HR=2.3; 95%CI:1.2–4.5; P=.008, respectively], and the presence of periapical radiolucency at the latest recall (HR=3.2; 95%CI:1.7–6.3; P<.001). Conclusions: The outcome of ETT restored with CMCPC was assessed to be very good. CMCPC may be regarded as a viable restorative option for ETT with reduced coronal structure.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".