Influence of Glycemic Control on Peri‐Implant Bone Healing: 12‐Month Outcomes of Local Release of Bone‐Related Factors and Implant Stabilization in Type 2 Diabetics
Bibliographic record
Abstract
BACKGROUND: The poor glycemic status seems to be an important factor affecting implant complication rates, including peri-implant bone loss. PURPOSE: This trial evaluated the influence of glycemic control of type 2 diabetes mellitus (T2DM) patients on implant stabilization and on the levels of bone markers in peri-implant fluid during the healing. MATERIALS AND METHODS: Systemically healthy patients (SH,n = 19), better-controlled T2DM (BCDM,n = 16), and poorly controlled T2DM (PCDM,n = 16) indicated for implant therapy were recruited. The implant stability quotient (ISQ) was determined at implant placement, 3, 6, and 12 months. Levels of transforming growth factor- β (TGF-β), fibroblast growth factor (FGF), osteopontin (OPN), osteocalcin (OC), and osteoprotegerin (OPG) in the peri-implant fluid were quantified at 15 days, and 3, 6, and 12 months, using the Luminex assay. RESULTS: OPG and OPN levels were higher in SH at 12 months than at15 days (p < .05), whereas OC and TGF-β were lower in PCDM at 12 months compared with the 15-day and 3-month follow-ups, respectively (p < .05). Inter-group analyses showed lower OPN levels in PCDM compared with SH at 12 months (p < .05). The ISQ was higher at 12 months when compared with baseline and 3 months in SH (p < .05), whereas no differences were observed during follow-up in diabetics, regardless of glycemic control (p > .05). No difference in ISQ was observed among groups over time (p > .05). CONCLUSION: Poor glycemic control negatively modulated the bone factors during healing, although T2DM, regardless of glycemic status, had no effect on implant stabilization.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".