Patient perspectives on the supervision of orthodontic retention
Bibliographic record
Abstract
INTRODUCTION The NHS contract dictates that orthodontic treatment providers supervise retention for 12 months. Typical protocols involve multiple face-to-face appointments during this period. The aim of this study was to explore the views of patients on the current and future direction of supervising retention. METHODS A local questionnaire was developed to assess patients’ perspectives on current review protocols and their associated burden, and the potential for alternative modes of review. Questionnaires were completed by 50 consecutive patients with removable retainers attending for review between May and July 2024. RESULTS Patients generally felt that retainer review appointments were important (90%). However, the burden of travelling to appointments meant that a quarter (28%) of patients estimated that this would result in missing more than 2 hours of education or employment. A majority (56%) of patients wanted other means of review to be offered. In particular, university students faced the largest burden of attending appointments and expressed the greatest desire for other modes of monitoring. CONCLUSIONS Accounting for patient lifestyle factors and the burden of attending appointments should supplement clinical aspects in determining the review protocols utilised. Further research investigating clinical and patient-reported outcomes with alternative supervision protocols is required.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".