A retrospective analysis of restorative factors that affect prognosis of endodontically treated teeth
Bibliographic record
Abstract
Although there have been many studies concerning endodontically treated teeth (ETT) and the factors affecting prognosis, many have reported different findings. This retrospective study aimed to support the hypothesis that the initial pulpal diagnosis and restorative factors can help determine the prognosis of ETT. The University of Manitoba’s data collecting software was used to assess 1,360 ETT from January 2011 to June 2021, a period of 10 years. A Kaplan-Meier survival estimate with an associated P value comparing different prognosis, types of posts, and restorations, respectively, was performed using SPSS statistical software. From this data pool, there was a 94.4% survival rate of ETT with only 5.6% failing. A pre-operative necrotic pulp diagnosis was determined to be clinically significant in affecting prognosis. In descending order, a full coverage crown proved to improve prognosis, then permanent restorations, then temporary restorations. Other factors such as the presence of a post, type of post, amalgam vs composite, and type of crown did not affect prognosis. In descending order of causing failure in ETT, reasons were: 33.33% non-restorable crown fractures, 25% vertical root fractures, 14.29% inadequate restorations, 10.71% periodontal reasons, 9.5% endodontic failure, and 7.14% non-restorable caries. Based on these results, it was concluded that just as the quality of endodontic treatment is important, so is the quality/type of restorations that follows. Based on this paper, patients should be recommended full coverage crowns after endodontic treatment to ensure the best prognosis.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".