Dental Implant Survival and Complication Rate over 2 Years for Individuals with Poorly Controlled Type 2 Diabetes Mellitus
Bibliographic record
Abstract
Abstract Background Emerging evidence suggests that implant therapy may be a viable option for diabetic individuals with elevated glycemic levels. Purpose The purpose of this 2 year observational study was to evaluate survival and clinical complications of dental implants following placement in type 2 diabetes individuals having poor glycemic control. Materials and Methods Adult participants (n = 24) with poorly controlled type 2 diabetes (8.0% ≤ HbA1c ≤ 12.0%) received two or more transgingival dental implants. Survival was evaluated after 1 (23 participants, 72 implants) and 2 (20 participants, 59 implants) years. Clinical complications were evaluated in 18 participants (52 implants) after 21‐34 months. Relationships between complications and stratified HbA1c levels were assessed using Pearson's correlation test. Results Survival rates were 98.6% (71/72 implants) after 1 year and 96.6% (57/59 implants) after 2 years. Complications were identified in 29% of participants with peri‐implant mucositis, the most common event. Complications correlated directly with number of implants across HbA1c strata (0.42, R2 = 0.66). There was no correlation between HbA1c and the occurrences of complications or mucositis. Conclusions This 2‐year evaluation supports the broader application of implant therapy in type 2 diabetes individuals with poor glycemic control in demonstrating high survival rates with limited complications.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".