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Record W7116713412 · doi:10.1308/rcsfdj.2026.8

Patient perspectives on the supervision of orthodontic retention

2025· article· en· W7116713412 on OpenAlexaboutno aff
Samuel Reeves, Gavin Mack

Bibliographic record

VenueFaculty Dental Journal · 2025
Typearticle
Languageen
FieldDentistry
TopicOrthodontics and Dentofacial Orthopedics
Canadian institutionsnot available
Fundersnot available
KeywordsRetainerQuarter (Canadian coin)MEDLINEPatient carePatient satisfaction

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.042
Threshold uncertainty score0.421

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.029
GPT teacher head0.309
Teacher spread0.280 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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