Sublingual Crushed Levodopa for Parkinson’s Disease Management of a Lewy Body Dementia Patient With Dysphagia: A Case Report
Bibliographic record
Abstract
Levodopa/carbidopa is the cornerstone treatment for Parkinson's disease (PD), but in patients with dysphagia, there are limited options for the routes of administration. Traditional methods of administering levodopa/carbidopa in these patients, such as rectal administration, have been shown to offer limited symptomatic relief and suboptimal pharmacokinetic outcomes. This case explores sublingual administration of levodopa/carbidopa as a novel solution for managing parkinsonian symptoms in dysphagic patients in the hospital setting, where non-oral options are often limited. We present an 85-year-old Caucasian male with Lewy body dementia and parkinsonian symptoms, who presented to the emergency department with dysphagia and altered level of consciousness in the context of a urinary tract infection. Due to a missed dose of levodopa/carbidopa, he exhibited worsening bradykinesia, rigidity, resting tremors, and dysphagia. Levodopa/carbidopa was crushed and administered sublingually. Symptom improvement and resolution were observed within two hours of the sublingual dose, and the patient was able to tolerate oral medications thereafter. This case demonstrates that sublingual administration of levodopa/carbidopa may be a practical and effective alternative approach for managing parkinsonian symptoms in patients with 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".