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Record W4394749132 · doi:10.3390/lubricants12040126

Oral Lubrication, Xerostomia, and Advanced Macromolecular Lubricants for Treatment of Dry Mouth

2024· article· en· W4394749132 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueLubricants · 2024
Typearticle
Languageen
FieldMedicine
TopicSalivary Gland Disorders and Functions
Canadian institutionsDalhousie University
Fundersnot available
KeywordsLubricityDry mouthSalivaLubricationMacromoleculeMedicineMaterials scienceChemistryComposite materialBiochemistryInternal medicine

Abstract

fetched live from OpenAlex

Dry mouth, also known as xerostomia, is a condition in which insufficient or ineffective saliva does not provide sufficient oral lubrication. The severity of this condition can vary from a mild discomfort to a debilitating condition that greatly impairs patients’ lives. Xerostomia arises as a side effect of various medications, diseases, radiation therapy, chemotherapy, or nerve damage. Various aqueous dispersions of macromolecules have been proposed to assist or replace the saliva in these patients. It is vital that these macromolecules have ample lubricity and water retention properties while showing long-lasting efficacy. The emphasis of this review is to provide a general overview on lubricating macromolecules that have been clinically used or reported in the literature as potential replacements for saliva. These include various natural or synthetic polymers, proteins, peptides, and lipids that are used in the form of solutions, gels, emulsions, and colloids. Perspectives into the future of macromolecular oral lubricants in the treatment of xerostomia are also provided.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.861
Threshold uncertainty score0.509

Codex and Gemma teacher scores by category

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

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.

Opus teacher head0.020
GPT teacher head0.298
Teacher spread0.278 · 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