Chronotherapy in dentistry: A scoping review
Bibliographic record
Abstract
The circadian clock modulates almost all vital aspects of our physiology and metabolism, including processes relevant to dentistry, such as healing, inflammation and nociception. Chronotherapy is an emerging field aiming to improve therapeutic efficacy and decrease adverse effects on health outcomes. This scoping review aimed to systematically map the evidence underpinning chronotherapy in dentistry and to identify gaps in knowledge. We conducted a systematic scoping search using four databases (Medline, Scopus, CINAHL and Embase). We identified 3908 target articles screened by two blinded reviewers, and only original animal and human studies investigating the chronotherapeutic use of drugs or interventions in dentistry were included. Of the 24 studies included, 19 were human studies and five were animal studies. Chrono-radiotherapy and chrono-chemotherapy reduced treatment side effects and improved therapeutic response, leading to higher survival rates in cancer patients. Animal studies reported that tooth movement and periodontal tissue response to orthodontic forces follow a diurnal rhythm that might influence bone metabolism. Profound and prolonged local anesthesia could be achieved when injected in the evening. Although the overall quality of the included studies was low, chronotherapy applications in dentistry seem to have favourable outcomes, especially in head and neck cancer treatments.
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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 | 0.007 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.018 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".