Delirium tremens in an AUD patient after an intrathecal baclofen pump induced total alcohol abstinence.
Bibliographic record
Abstract
OBJECTIVE: Delirium Tremens (DT) is the most severe complication of alcohol withdrawal syndrome (AWS), and has a mortality rate of 1-5%. Baclofen is recommended for spasticity treatment, but it has recently been used for alcohol withdrawal symptoms reduction and alcohol abstinence. CASE REPORT: A cervical spinal cord injury patient was treated for two years with oral baclofen 80 mg/day for spasticity. He is alcohol-dependent and a cannabis user and required an intrathecal baclofen (ITB) pump implant. A week after the implant, he stopped drinking, as "he didn't felt the urge anymore". The AWS appeared five days after the last alcohol intake and DT at 7 days. Diazepam 20 mg was used up to three times per day, but didn't seem to improve or reduce the anxiety, agitation, visual or auditory hallucinations. Two years later the patient remains alcohol abstinent and still on intrathecal baclofen. CONCLUSIONS: Alcohol-dependent patients can abruptly stop their alcohol intake, while in continuous infusion of intrathecal baclofen. Baclofen can be useful in the acute treatment of AWS as it seems to reduce diazepam requirements and in long-term alcohol abstinence. In the presence of AWS, while on chronic baclofen, no dose reduction should be attempted, as it can worsen the AWS or trigger baclofen withdrawal.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| 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".