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Xerostomia: Clinical Aspects and Treatment

2003· review· en· W2046366691 on OpenAlexaff
Sandra F. Cassolato, Robert S. Turnbull

Bibliographic record

VenueGerodontology · 2003
Typereview
Languageen
FieldMedicine
TopicSalivary Gland Tumors Diagnosis and Treatment
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMedicineDry mouthPolypharmacyIntensive care medicineSwallowingMedical prescriptionPopulationDenturesAnticholinergicDentistryMalnutritionOral hygieneInternal medicineSaliva

Abstract

fetched live from OpenAlex

Xerostomia or dry mouth is a condition that is frequently encountered in dental practice. The most common cause is the use of certain systemic medications, which make the elderly at greater risk because they are usually more medicated. Other causes include high doses of radiation and certain diseases such as Sjogren's syndrome. Xerostomia is associated with difficulties in chewing, swallowing, tasting or speaking. This results in poor diet, malnutrition and decreased social interaction. Xerostomia can cause oral discomfort, especially for denture wearers. Patients are at increased risk of developing dental caries. A thorough intraoral and extra-oral clinical examination is important for diagnosis. Treatment may include the use of salivary substitutes (Biotene), salivary stimulants such as pilocarpine, ongoing dental care, caries prevention, a review of the current prescription drug regimen and possible elimination of drugs having anticholinergic effects. Because of the ageing population, and the concomitant increase in medicated individuals, dentists can expect to be presented with xerostomia in an increasing number of patients in the coming years and therefore should be familiar with its diagnosis and treatment. Therefore, the purpose of this review is to outline for clinicians the common aetiologies, clinical identification, and routine therapeutic modalities available for individuals with xerostomia.

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 score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.002

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.

Opus teacher head0.210
GPT teacher head0.458
Teacher spread0.248 · 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

Citations348
Published2003
Admission routes1
Has abstractyes

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