Labels and descriptions of dental behaviour support techniques: A scoping review of clinical practice guidelines
Bibliographic record
Abstract
INTRODUCTION: There is no agreed taxonomy of the techniques used to support patients to receive professional oral healthcare. This lack of specification leads to imprecision in describing, understanding, teaching and implementing behaviour support techniques in dentistry (DBS). METHODS: This review aims to identify the labels and associated descriptors used by practitioners to describe DBS techniques, as a first step in developing a shared terminology for DBS techniques. Following registration of a protocol, a scoping review limited to Clinical Practice Guidelines only was undertaken to identify the labels and descriptors used to refer to DBS techniques. RESULTS: From 5317 screened records, 30 were included, generating a list of 51 distinct DBS techniques. General anaesthesia was the most commonly reported DBS (n = 21). This review also explores what term is given to DBS techniques as a group (Behaviour management was most commonly used (n = 8)) and how these techniques were categorized (mainly distinguishing between pharmacological and non-pharmacological). CONCLUSIONS: This is the first attempt to generate a list of techniques that can be selected for patients and marks an initial step in future efforts at agreeing and categorizing these techniques into an accepted taxonomy, with all the benefits this brings to research, education, practice and 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 | 0.040 | 0.145 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.035 | 0.029 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".