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Record W3106840539

Medical management, orofacial findings, and dental care for the client with major depressive disorder.

2019· article· en· W3106840539 on OpenAlexaff
Aviv Ouanounou, Kester Ng

Bibliographic record

VenuePubMed · 2019
Typearticle
Languageen
FieldMedicine
TopicSalivary Gland Disorders and Functions
Canadian institutionsToronto General HospitalUniversity of Toronto
Fundersnot available
KeywordsIrritabilityDepression (economics)MedicineSadnessPharmacotherapyPsychiatryManagement of depressionAlternative medicineAnxiety
DOInot available

Abstract

fetched live from OpenAlex

Depression is a common mental illness affecting millions worldwide. It is characterised by several symptoms including persistent sadness, constant irritability, and the loss of interest in pleasurable activities. The medical management of depression includes psychotherapy and pharmacotherapy. Depression is associated with numerous oral findings such as diminished salivary flow, rampant dental decay, advanced periodontal disease, and oral dysesthesias. Many of the oral findings in clients with depression can be due to the disease itself or to the treatment used for the condition; it can be difficult to determine which came first. Dental practitioners need to be aware of these orofacial findings and to treat and manage these clients appropriately. This short communication reviews the pharmacotherapy of depression and the effects of the drugs commonly used. Necessary dental treatment modifications for clients with depression are discussed.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.006
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.001

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.

Opus teacher head0.008
GPT teacher head0.224
Teacher spread0.216 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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

Citations3
Published2019
Admission routes1
Has abstractyes

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