Gum chewing improves swallow frequency and latency in Parkinson patients
Bibliographic record
Abstract
BACKGROUND: Reduced swallowing frequency affects secretion management in Parkinson disease (PD). Gum chewing increases saliva flow and swallow frequency. This study uses chewing gum to modify swallow frequency and latency between swallows in patients with PD. OBJECTIVES: 1) Assess the frequency and latency of swallow at baseline (BL), during gum chewing (GC), and post gum chewing (PGC) for participants with PD (stage 2-4) nonsymptomatic for prandial dysphagia; and 2) assess carryover after gum is expectorated. METHODS: Twenty participants were studied across 3 tasks, each of 5 minutes in duration: BL, GC, and PGC. Respiratory and laryngeal signals were continuously recorded using PowerLab (version 5.5.5; ADI Instruments, Castle Hill, Australia). Frequency and latency of swallow events were calculated. RESULTS: Differences (analysis of variance) are reported for frequency (p < 0.000001) and latency (p < 0.000001). Swallow frequency (mean +/- SD) increased during GC (14.95 +/- 3.02) compared with BL (3.1 +/- 2.85) and PGC (7.0 +/- 2.57). Latency in seconds (mean +/- SD) decreased during GC (24.1 +/- 4.174) and increased with BL (131.8 +/- 59.52) and PGC (mean = 60.74 +/- 25.25). Intertask comparisons (t test) found differences in swallow frequency and latency between tasks: BL vs GC (p < 0.0001, p < 0.0001), BL vs PGC (p < 0.0011, p < 0.0009), and GC vs PGC (p < 0.0001, p < 0.0002), respectively. Post hoc analysis showed carryover to 5.317 minutes. CONCLUSIONS: Modifying sensorimotor input by chewing gum alters frequency and latency of swallowing and may be an effective strategy for secretion management in Parkinson disease. CLASSIFICATION OF EVIDENCE: This study provides Class III evidence that chewing gum increases swallow frequency and decreases latency of swallowing in an experiment in patients with stage 2 to 4 Parkinson disease who are nonsymptomatic for significant prandial dysphagia.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".