Oral health impacts of medications used to treat mental illness
Bibliographic record
Abstract
BACKGROUND: Many psychotropic medications affect oral health. This review identified oral side effects for antidepressant, antipsychotic, anticonvulsant, antianxiety and sedative drugs that are recommended in Australia for the management of common mental illnesses and provides recommendations to manage these side-effects. METHODS: The Australian Therapeutic Guidelines and the Australian Medicines Handbook were searched for medications used to treat common mental health conditions. For each medication, the generic name, class, and drug company reported side-effects were extracted from the online Monthly Index of Medical Specialties (eMIMs) and UpToDate databases. Meyler's Side Effect of Drugs Encyclopaedia was used to identify additional oral adverse reactions to these medications. RESULTS: Fifty-seven drugs were identified: 23 antidepressants, 22 antipsychotics or mood stabilisers, and 12 anxiolytic or sedative medications. Xerostomia (91%) the most commonly reported side effect among all classes of medications of the 28 identified symptoms. Other commonly reported adverse effects included dysguesia (65%) for antidepressants, and tardive dyskinesia (94%) or increased salivation (78%) for antipsychotic medications. CONCLUSIONS: While xerostomia has often been reported as a common adverse effect of psychotropic drugs, this review has identified additional side effects including dysguesia from antidepressants and tardive dyskinesia and increased salivation from antipsychotics. Clinicians should consider oral consequences of psychotropic medication in addition to other side-effects when prescribing. For antidepressants, this would mean choosing duloxetine, agomelatine and any of the serotonin re-uptake inhibitors except sertraline. In the case of antipsychotics and mood stabilisers, atypical agents have less oral side effects than older alternatives.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".