The effect of post‐extraction socket preservation laser treatment on bone density 4 months after extraction: Randomized controlled trial
Bibliographic record
Abstract
BACKGROUND: Post-extraction bone resorption may affect the outcome of ensuing restorations. PURPOSE: This study aimed to evaluate a comprehensive laser post-extraction protocol by comparing resulting alveolar bone regeneration with that obtained after standard extraction procedure. MATERIALS AND METHODS: About 53 simple extractions were randomized to either laser or control group. In the laser group, erbium (Er:YAG; 2940 nm) and neodymium (Nd:YAG; 1064 nm) lasers were used for degranulation, disinfection, de-epithelialization of the surrounding gingiva, clot stabilization, and photobiomodulation. The primary outcome measure was change in bone density in the extraction area between day 1 and 4 months after extraction. Patients were monitored for potential side effects. RESULTS: Increase in bone density at the follow-up CBCT was significantly higher in laser than in control group (p < 0.001). No post-operative pain, bleeding, or swelling was present in the laser group. In the control group, one patient had bleeding 3-5 days after extraction, two patients had swelling and three patients reported post-operative pain rated 3-5 on a 0-10 pain scale up to 3 days after extraction. CONCLUSIONS: The proposed laser post-extraction procedure is a safe and effective method to improve post-extraction bone healing.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".