Effect of low level laser therapy on crestal bone levels around dental implants—A pilot study
Bibliographic record
Abstract
Abstract Background Implant success is affected by initial bone resorption at the implant surface. Continuous efforts have been made to reduce the peri‐implant crestal bone loss. Limited information is available regarding the influence of low level laser therapy (LLLT) on interaction between the bone and implant surface. Purpose The aim of this pilot study was to assess the effect of LLLT on peri‐implant crestal bone levels. Materials and methods Twenty implants were placed in 20 patients who were randomly assigned to two groups. Group I patients' received no adjunctive treatment and group II patients' were administered LLLT using 980 nm diode laser at 0.1 W output power following implant placement. The energy density of 4 J/cm 2 was delivered at six sites for a duration of 10 seconds per site. Crestal bone levels were evaluated primarily using digital intraoral periapical (IOPA) radiograph. The measurements were made immediately (T0) and 6 weeks (T1) post implant placement; and 6 months (T2) and 1 year (T3) post prosthetic loading time intervals and compared using repeated measures ANOVA test. Results Crestal bone levels at baseline were statistically not significant between groups ( P = .880). At T3 time interval, the mean change in crestal bone levels around all anatomical implant sites measured was 0.81 (SE 0.04) mm for irradiated group and 0.97 (SE 0.04) mm for nonirradiated group. Intergroup analysis revealed statistically significant ( P = .020) less crestal bone loss in group that received LLLT. Conclusion Under the conditions of this study, LLLT reduced the crestal bone resorption surrounding dental implants. Trial registration The present clinical trial was not registered.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".