Perception and Impact of White Spot Lesions in Young People Undergoing Orthodontic Treatment and Their Guardians: Protocol for a Mixed Methods Study
Bibliographic record
Abstract
Background: White spot lesions (WSLs) are white marks that can form on teeth during orthodontic treatment with fixed appliances and become apparent once they are removed. About half of people who have fixed appliance treatment get WSLs. They are usually caused by poor toothbrushing around the brace. Although there have been studies that have investigated the prevention and treatment of WSL, there remain uncertainties about what young people and their parents or guardians know or feel about them. A Cochrane review concluded that patient-reported outcomes have been overlooked in WSL prevention studies. Objective: The aim of this study is to explore young people's and their parents'/guardians' perceptions, attitudes, and feelings toward WSLs using a mixed methods study. Methods: This is a mixed methods study. Part 1 is a cross-sectional survey using a web-based survey questionnaire and images of pretreatment malocclusions and postorthodontic WSLs of varying severity (mild, moderate, severe). Part 2 will involve one-to-one, semistructured interviews, using open-ended questions with young people and their parents/guardians. Participants will be recruited from patients aged 11-15 years before, during, or after undergoing orthodontic treatment at Liverpool University Dental Hospital and their parents/guardians. Part 1 (quantitative) will use a Likert scale with the option of free text comments. Data will be analyzed using descriptive statistics. Agreement between participants will be analyzed using the κ statistic. Part 2 (qualitative) will be analyzed using a modified framework analysis approach; the outcomes will be presented as themes. Transcripts from the qualitative interview will be analyzed using inductive thematic analysis. Once the qualitative and quantitative data have been analyzed, we will combine the two datasets and compare them for convergence or divergence. We will aim for a sample size of at least 100 participant and parent/guardian pairs for Part 1 and 30 interviewees for Part 2. Ethical approval was granted in November 2024. The Sponsor Permission to Proceed notification was received in January 2025. Results: Funding for the study was secured in May 2024. Recruitment started on February 2, 2025. As of August 31st, 2025, seventy five participant pairs have been recruited. Conclusions: The study will increase understanding of the impact WSLs have on oral health-related quality of life and the decision-making of young people and their parents/guardians.
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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 | 0.001 | 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".