Xerostomia: From Pharmacological Treatments to Traditional Medicine—An Overview on the Possible Clinical Management and Prevention Using Systemic Approaches
Bibliographic record
Abstract
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.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
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".