Effect of LED photobiomodulation on dental implant osseointegration: An in vivo study
Bibliographic record
Abstract
Background. Photobiomodulation (PBM) may be prescribed after dental surgery to accelerate tissue healing and improve implant stability. The objective of this study is to evaluate the efficiency of LED-PBM on the dental implant osseointegration. Methods. A total of 48 implants (KontactTM) were inserted in 8 Yucatan minipigs (6 implants per minipig) divided into 2 groups (test and control). The test group received LED-PBM with a total energy of 124.2 J/cm2 delivered over 4 sessions (at day0, day+8, day+15 and day+28) lasting 12 minutes each. At day+28, all animals were sacrificed, and their mandibles removed to perform histologic and histomorphometric analysis. Implant osseointegration was evaluated using the computation of bone/implant contact (BIC) index and bone surface/total surface (BS/ TS) ratio. The groups were compared using Student’s unpaired t test. Results. BIC index and BS/TS ratio were significantly higher within the test group as compared to the control group (P<0.01). Histologic observations on bone tissues demonstrated that LED-PBM may improve and accelerate dental implant osseointegration: 25% of dental implants analyzed within the test group were completely osseointegrated, versus 12.5% within the control group. Conclusion. This experimental study indicates that LED-PBM contributes to enhancing implant treatment outcomes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".