Pretreatment With Ibuprofen to Prevent Electroconvulsive Therapy–Induced Headache
Bibliographic record
Abstract
BACKGROUND: Although electroconvulsive therapy (ECT) has been widely recognized as an effective treatment for severe depression and various other psychiatric illnesses, adverse effects have been frequently reported, especially a high incidence of headache. Analgesics, such as acetaminophen, narcotics, or nonsteroidal anti-inflammatory drugs (NSAIDs), are commonly used to treat ECT-induced headache. The objective of this study was to determine whether pretreatment with ibuprofen would prevent the onset or decrease the severity of headache that occurs after ECT. METHOD: All inpatients on the psychiatric units who required ECT treatment were asked to participate in the study. Thirty-four patients were randomly assigned to receive either ibuprofen, 600 mg, or placebo orally 90 minutes prior to the initial ECT session, with the alternate treatment given for the second ECT treatment. Patients were asked to complete a questionnaire prior to and after the first 2 ECT treatments regarding the pattern, severity, and onset of headache. Severity of the headache was measured on a visual analogue scale (VAS). RESULTS: Ten patients experienced headache in neither treatment arm, while 7 patients experienced headache in both treatment arms. Eleven patients experienced headache with placebo but not with ibuprofen, while 2 patients experienced headache with ibuprofen but not with placebo. Ibuprofen was significantly more effective than placebo in preventing the onset of headache post-ECT (p =.022). The mean +/- SD VAS headache scores were 1.49 +/- 1.54 and 0.54 +/- 0.91 in the placebo and ibuprofen arms, respectively. Ibuprofen was significantly more effective than placebo in reducing the severity of ECT-induced headache (p =.007). CONCLUSION: Ibuprofen premedication reduced the frequency and severity of headache post-ECT and should be considered for appropriate patients who suffer from ECT-induced headache.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".