Type 2 diabetes affects postextraction socket healing and influences first‐stage implant surgery: A study based on clinical and animal evidence
Bibliographic record
Abstract
AIM: To verify the influence of type 2 diabetes mellitus (T2DM) on postextraction socket healing and subsequent first-stage implant surgery. MATERIALS AND METHODS: We analyzed pre-extraction and postextraction cone beam computed tomography images of T2DM patients (n = 75) and paired nondiabetic controls to investigate changes in postextraction socket and ridge dimensions. The types of guided bone regeneration (GBR) surgeries were also compared. Three T2DM pig models were established to compare their postextraction socket healing with that of nondiabetic controls. Healing was quantitatively verified by microcomputed tomography. The osteogenic differentiation of mesenchymal stem cells (MSCs) was also compared. RESULTS: Compared to nondiabetic controls, T2DM patients had higher socket width/depth values postextraction across all groups with different healing times. Among the T2DM patients, 62.7% could not receive first-stage implant surgery within 6 months postextraction, and 54.7% received GBR surgery during first-stage surgery. Ossification was not achieved in the socket center of the T2DM pig models after 3 months of healing. A decrease in osteogenic differentiation was observed in T2DM-MSCs. CONCLUSIONS: T2DM interferes with the healing of the extraction socket and thus delays first-stage implant surgery. This phenomenon may be due to the reduced osteogenic differentiation of MSCs in the sockets.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".