Effectiveness of saliva substitute products in the treatment of dry mouth in the elderly: a pilot study
Bibliographic record
Abstract
The aging population is susceptible to developing dry mouth (xerostomia). Elderly patients present all of the major risk factors to acquiring dry mouth which include systemic diseases and disorders, such as diabetes and depression, and the use of numerous medications, including anti-hypertensives and anti-depressants. The consequences of untreated dry mouth are severe limitations of masticatory function and speech, and increased risk of developing caries, periodontal diseases and fungal infections. Assessment of xerostomia, which includes a set of signs and symptoms that impact on the individual, can only be fully explored through a thorough medical history, intra-oral examination and recording the subjective views of patients. This study suggests a methodology for the assessment of xerostomia through a xerostomia questionnaire, which was used to evaluate the effectiveness and acceptability of a saliva substitute product (Biotène) in the treatment of xerostomia in 20 elderly patients exhibiting both severe and moderate symptoms. Wilcoxon signed-ranked tests revealed significant improvements in the number and severity of symptoms between the pre-test and the post-test groups. Biotène products were also found to be effective in the treatment of both severe and moderate symptoms of xerostomia. Biotène saliva substitutes are an acceptable and effective method of treatment for elderly people suffering from dry mouth.
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 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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".