Zopiclone and Prazepam Abuse in a Chronic Migraine Patient
Bibliographic record
Abstract
Painful conditions are frequently accompanied by sleep disorders. Here we present the case, never previously reported, of high doses of zopiclone and prazepam abuse in a patient suffering from migraine and insomnia. A 37-year-old female Caucasian patient, nursing degree, with two 7-year-old twin daughters, began to suffer from migraines in her childhood. Migraine worsened and was associated with insomnia after the daughters’ birth. Eight months ago, migraine and insomnia further worsened in conjunction with the prescription of fluoxetine and prazepam as migraine prophylaxis and of zopiclone for insomnia. From that moment, the patient increased the dose of zopiclone up to 20 tablets (150 mg) nightly and prazepam up to 20 tablets (400 mg) daily. As acute migraine treatment she also used ketorolac 1-2 f every day intramuscularly. The patient was hospitalised for withdrawal of medications and to treat migraine. Hair analysis documented the history of this patient and the good progress of the treatment. Zopiclone and prazepam are considered drugs with low abuse potential. However, the final outcome of a drug treatment may be influenced by non-drug factors, such as the patient’s characteristics and the treatment milieu. In fact, in a stressful context, the prescription of zopiclone in a patient vulnerable for migraine triggered abuse of this drug and of prazepam, which was associated with chronification of migraine and analgesics overuse, without relieving insomnia. In prescribing hypnotics and anxiolytics to patients with chronic pain there must therefore be great caution. doi:10.4021/jmc729w
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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) | 0.000 | 0.000 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".