Dental and medical management of the patient with bipolar disorder
Bibliographic record
Abstract
OBJECTIVE: The objective of this literature review is to understand the appropriate medical management of patients with bipolar disorder (BD) which subsequently can translate into the effective dental management of patients suffering with this illness METHODS: This study was completed using three databases which included PubMed, Google Scholar and Cochrane library. Additionally, relevant dental and medical textbooks were also used to summarize more complex topics regarding BD. Descriptors used to find relevant articles included "Bipolar Disorder", "Adverse drug effects of bipolar medications", and "Orofacial findings with patients with bipolar disorder". A total of 49 relevant articles and textbooks were found which were included in this literature review. RESULTS: BD is a mental illness which affects millions worldwide. It is characterized by alternating episodes of mania and depression. During the manic phase there is an abnormal elevation in mood whereas the depressive episodes consist of the opposite. The medical management of BD involves pharmacotherapy and psychotherapy. Common dental findings in patients with BD include caries, periodontal disease, xerostomia and adverse oral effects from the medications used to manage this illness. CONCLUSION: BD has a significant burden on society and to effectively manage their medical and dental needs, the clinician must be well versed in their illness.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".