The Effect of Cancer and Its Management on Orthodontic Treatment Outcomes: Systematic Review
Bibliographic record
Abstract
Orthodontic treatment for patients undergoing cancer therapy presents unique challenges due to the effects of chemotherapy and radiotherapy on oral health. The impact of oncological treatments on orthodontic outcomes, including dental development, malocclusions, and treatment success, remains an area of ongoing research. This systematic review aim to evaluate the influence of radiotherapy and anti-neoplastic drugs on the successful completion of orthodontic treatment plans.A comprehensive literature search was conducted across six databases following PRISMA guidelines. The PICOS framework guided the study selection, focusing on individuals with dental or occlusal issues undergoing or recovering from cancer treatment. Eligible studies included retrospective, case-control, prospective case-control, and cross-sectional designs. The review included studies from various regions with diverse sample sizes. Findings indicated that 60% of children receiving chemotherapy achieved successful orthodontic outcomes, though chemotherapy significantly reduced treatment efficacy for certain malocclusions. Radiation therapy was associated with an increased risk of root resorption and microdontia compared to chemotherapy. Cancer treatments significantly influence orthodontic treatment success, with chemotherapy and radiotherapy impacting dental outcomes in distinct ways. While orthodontic treatment remains viable for cancer patients, individualized planning and multidisciplinary collaboration are crucial to optimizing results. Future research should focus on larger prospective studies to refine treatment guidelines and address confounding factors affecting orthodontic outcomes in oncological patients.
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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 | 0.006 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".