Impact of intraoral non‐pharmacological non‐surgical adjunctive interventions on orthodontically induced inflammatory root resorption in humans: A systematic review
Bibliographic record
Abstract
BACKGROUND: The current systematic review aimed to assess the impact of intraoral non-surgical non-pharmacological adjunctive interventions on orthodontically induced inflammatory root resorption (OIIRR). SEARCH METHODS: Search without restrictions was performed up to November 2020 in three electronic databases (Cochrane Central Register of Controlled Trials, MEDLINE and EMBASE) for randomized controlled trials (RCTs), prospective and retrospective non-randomized studies. The ROB 2.0 tool was used to assess the quality of the included RCTs, and the ROBINS-I tool was applied to non-randomized clinical studies. The strength of evidence was ranked using GRADE. RESULTS: Three hundred and sixteen records were initially retrieved. A total of 10 studies, with 236 patients, were finally considered. These studies assessed the effects of mechanical vibration (low-frequency and high-frequency), low-intensity pulsed ultrasound (LIPUS), low-level laser therapy (LLLT) and photobiomodulation (light-emitting devices (LED). While the low-frequency vibration and LED do not seem to affect OIIRR, OIIRR has been reported to be reduced in high-frequency vibration, and LIPUS-treated teeth (differences may not likely be considered clinically relevant). The potential positive effect of LLLT on OIIRR is still debatable. Overall, the existing evidence suggests that the amount of OIIRR observed while using these interventions with traditional orthodontic treatment was not more than that was observed without it. CONCLUSIONS: Based on a very low level of confidence, it seems that intraoral non-pharmacological non-surgical adjunctive interventions do not affect the amount of OIIRR either positively or negatively to a clinically relevant degree when compared to what is seen with conventional orthodontic treatment alone.
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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 | 0.008 | 0.032 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.008 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".