Periodontal considerations in orthodontic treatment: A review of the literature and recommended protocols
Bibliographic record
Abstract
Orthodontic treatment can preserve, harm or benefit the periodontal condition. During orthodontic treatment, patients may be at a greater risk of developing periodontal disease or conditions. Thus, the treating clinicians should carefully evaluate their patients' periodontal conditions prior to, during, and after the completion of orthodontic treatment. This literature review describes the damage situations and the methods of preventing and repairing them as well as situations in which the orthodontic intervention contributes to the periodontal status. Recommended protocols for periodontal screening, maintenance and follow-up are presented as well. Clinicians should conduct a complete periodontal evaluation prior to initiating orthodontic treatment, help patients develop an effective home care therapy regimen, ensure the absence of periodontal disease and or mucogingival deformities and conditions that may worsen during orthodontic treatment, and determine the appropriate recall maintenance program for the patient. During the active orthodontic phase, patients' periodontal conditions should be carefully monitored, compared to their baseline clinical and radiographic measurements, and treated if necessary. Patients' compliance with the suggested home oral care should be carefully evaluated and reinforced. If necessary, the orthodontic treatment may need to be stopped temporarily until patients' home oral care improves.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".