A Guide to Medications Inducing Salivary Gland Dysfunction, Xerostomia, and Subjective Sialorrhea: A Systematic Review Sponsored by the World Workshop on Oral Medicine VI
Bibliographic record
Abstract
BACKGROUND: Medication-induced salivary gland dysfunction (MISGD), xerostomia (sensation of oral dryness), and subjective sialorrhea cause significant morbidity and impair quality of life. However, no evidence-based lists of the medications that cause these disorders exist. OBJECTIVE: Our objective was to compile a list of medications affecting salivary gland function and inducing xerostomia or subjective sialorrhea. DATA SOURCES: Electronic databases were searched for relevant articles published until June 2013. Of 3867 screened records, 269 had an acceptable degree of relevance, quality of methodology, and strength of evidence. We found 56 chemical substances with a higher level of evidence and 50 with a moderate level of evidence of causing the above-mentioned disorders. At the first level of the Anatomical Therapeutic Chemical (ATC) classification system, 9 of 14 anatomical groups were represented, mainly the alimentary, cardiovascular, genitourinary, nervous, and respiratory systems. Management strategies include substitution or discontinuation of medications whenever possible, oral or systemic therapy with sialogogues, administration of saliva substitutes, and use of electro-stimulating devices. LIMITATIONS: While xerostomia was a commonly reported outcome, objectively measured salivary flow rate was rarely reported. Moreover, xerostomia was mostly assessed as an adverse effect rather than the primary outcome of medication use. This study may not include some medications that could cause xerostomia when administered in conjunction with others or for which xerostomia as an adverse reaction has not been reported in the literature or was not detected in our search. CONCLUSIONS: We compiled a comprehensive list of medications with documented effects on salivary gland function or symptoms that may assist practitioners in assessing patients who complain of dry mouth while taking medications. The list may also prove useful in helping practitioners anticipate adverse effects and consider alternative medications.
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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 | 0.013 | 0.032 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.034 | 0.022 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 0.002 |
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.
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".