Delirium following pregabalin discontinuation in an individual with no psychiatric or substance use history
Bibliographic record
Abstract
We report about a man in his mid-50s who was prescribed pregabalin (150 mg/day) for neuropathic pain due to a herniated intervertebral disc. Four weeks later, he presented to the emergency room with symptoms consistent with delirium. After ruling out acute intoxication with a substance and neurological causes, collateral information from the family and review of his medical chart indicated potential discontinuation syndrome owing to pregabalin. Following the successful treatment and resolution of delirium, the patient revealed he had been consistently consuming pregabalin doses upwards of 2 g/day over the past 2 weeks, leading to the premature exhaustion of his prescription and an abrupt cessation. The case findings underscore the necessity for physicians to recognise the potential for pregabalin misuse and the associated withdrawal risks, including delirium.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".