Effects of diode laser irradiation and fibroblast growth factor on periodontal healing of replanted teeth after extended extra‐oral dry time
Bibliographic record
Abstract
AIM: The search for effective protocols to reduce the incidence of root resorption and allow periodontal ligament repair is still challenging, given the unpredictable outcome of late tooth replantation. The aim of this study was to assess the effects of both high-power diode laser irradiation (DL) and basic fibroblast growth factor (FGF) on the periodontal healing of replanted teeth after extended extra-oral dry time. METHODS: Maxillary incisors of 50 male rats were extracted and assigned to three experimental and two control groups (n = 10). DL: root surfaces treated with DL (810 nm, continuous mode, 1.0 W, 30 s), FGF: topical application of FGF gel to the root surface and in the alveolar wound, DL + FGF: DL and topical application of FGF gel, C+: no treatment after extraction and immediate replantation and C-: no treatment after extraction and replantation after 60 min. In the experimental groups, the specimens were kept dry for 60 min, the pulps were removed and the canals were filled with calcium hydroxide paste prior to tooth replantation. The animals were euthanized after 60 days. The specimens were processed for radiographic, histological and immunohistochemical analyses. RESULTS: The radiographic analysis showed fewer resorptive areas in DL + FGF (P < 0.05). The histological and immunohistochemical analyses showed that the DL group had lower mean values of ankylosis, replacement and inflammatory resorption when compared to C-, not differing statistically from C+. DL + FGF produced significantly more collagen fibers (type I and type III) than C-, not differing from C+ in the case of type I fibers (P < 0.05). CONCLUSIONS: DL, with or without FGF, reduced the occurrence of external root resorption and ankylosis. Periodontal healing was favored and some fiber reinsertion occurred only when FGF was used.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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) | 0.000 | 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".