Prevalence of root canal treatments among diabetic patients: systematic review and meta-analysis
Bibliographic record
Abstract
(1) Apical periodontitis (AP) is the inflammatory response of the periapical tissue to bacterial antigens and toxins arriving from inside the root canal after pulp necrosis. To control AP, it is necessary to interrupt the passage of antigens from the root canal to the periapex, which is \n achieved via a root canal treatment (RCT), which is the indicated endodontic therapy in cases of AP. \n The prevalence of root-filled teeth (RFT) is an indicator of the frequency of endodontic infections and the degree of dental care. Diabetes is associated with AP and has been identified as the main prognostic factor in RCT. The aim of this study was to carry out a systematic review with meta analysis answering the following question: What is the prevalence of RFT among diabetic patients? \n (2) This study was conducted following the Preferred Reporting Items for Systematic Reviews and meta-analyses (PRISMA) guidelines 2020. A literature search was undertaken without limits on time All studies reporting the prevalence of RFT among diabetic patients via radiographic examination; both panoramic and periapical radiographs were included. Meta-analyses were calculated with Open Meta Analyst \n software. The main outcome variable was the prevalence of RFT, calculated as the total number of RFT divided by the total number of teeth, which is expressed as a percentage. As a secondary outcome variable, the prevalence of diabetic patients with at least one RFT, expressed as a percentage, was also calculated. The quality of evidence of the included studies was analyzed according to the guidelines provided by the Centre for Evidence-Based Medicine in Oxford. The risk of bias was assessed using the Newcastle–Ottawa Scale, which was adapted for cross-sectional studies. To estimate the variance and heterogeneity amongst the trials, the Higgings I2 test was employed. \n (3) Eight studies fulfilled the inclusion criteria. Four studies were classified as having a high risk of bias, and four were classified as having a moderate risk of bias. The prevalence of RFT was estimated for 37,922 teeth and 1532 diabetic patients. The overall calculated prevalence of RFT among diabetic patients was 5.5% (95% CI = 4.1–6.9%; p < 0.001). The percentage of diabetics who had at least one RFT was42.7% (95% CI =23.9–61.4%; p < 0.001). (4) This systematic review and meta-analysis concluded that the prevalence of RFT among diabetic patients is 5.5%. More than 40% of diabetics \n have at least one RFT. In daily clinics, dentists should suspect that patients are undiagnosed diabetics when multiple RCT failures are observed in the same patient.
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 | 0.011 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.028 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".