Treatment of hiccups in stroke rehabilitation with gabapentin: A case series and focused clinical review
Bibliographic record
Abstract
BACKGROUND: Persistent and intractable hiccups have a major impact on quality of life and can be a barrier to stroke rehabilitation. The first-line treatment for intractable hiccups, chlorpromazine, can have sedating effects, which may negatively affect rehabilitation participation. Gabapentin has been reported in several cases to be effective in hiccup treatment in both the general and post-stroke populations. OBJECTIVE: To describe the use of gabapentin for treatment of persistent or intractable hiccups in post-stroke patients. METHODS: Four cases were identified by clinicians for a retrospective review. A literature review was concurrently conducted. RESULTS: This case series presents four patients with improvement or resolution of intractable hiccups on gabapentin in a stroke rehabilitation setting. Therapeutic dose ranged from 100 mg TID to 400 mg BID. Treatment duration ranged from 2 days to 5.5 weeks. Adjuncts were used in three of the cases. A potential side effect was worsened confusion in one case. CONCLUSION: Evidence on the use of gabapentin for persistent or intractable hiccups is limited. This case series expands on the current literature by examining and comparing the current literature to our cases and exploring issues related to dosing, titration, side effects, and adjuncts to gabapentin.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".