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Record W4366975055 · doi:10.3390/curroncol30050336

Xerostomia: From Pharmacological Treatments to Traditional Medicine—An Overview on the Possible Clinical Management and Prevention Using Systemic Approaches

2023· review· en· W4366975055 on OpenAlexvenueno aff
Luigi Sardellitti, Antonella Bortone, Enrica Filigheddu, Francesca Serralutzu, Egle Milia

Bibliographic record

VenueCurrent Oncology · 2023
Typereview
Languageen
FieldMedicine
TopicSalivary Gland Disorders and Functions
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineIntensive care medicineClinical trialConcomitantDiseaseAmifostineClinical PracticeHead and neck cancerHead and neckPharmacologyCancerRadiation therapyInternal medicinePhysical therapySurgery

Abstract

fetched live from OpenAlex

Despite high incidence rates and severe complications, the management of xerostomia lacks clinical guidelines. The aim of this overview was to summarize the clinical experience derived from the last 10 years of treatments and prevention using systemic compounds. Results showed that the cytoprotective drug amifostine, and its antioxidant agents, are the most discussed as preventive agents of xerostomia in head and neck cancer (HNC) patients. In the presence of the disease, the pharmacological treatments have been mainly directed to stimulate secretion of the damaged salivary glands, or to counteract a decreased capacity of the antioxidant system, in view of an increasing of reactive oxygen species (ROS). However, the data demonstrated low ability of the drugs, together with a great number of side effects, which strongly limit their use. Concerning traditional medicine (TM), valid clinical trials are so limited that neither the efficacy nor the absence of interferences to concomitant chemical therapies can be validated. Consequently, the management of xerostomia and its devastating complications remain a very significant void in daily clinical practice.

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 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)0.001
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: Review · Consensus signal: Review
Teacher disagreement score0.983
Threshold uncertainty score0.835

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.901
GPT teacher head0.596
Teacher spread0.305 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
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

Citations30
Published2023
Admission routes1
Has abstractyes

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