Effectiveness and Safety of Propofol at Low Doses for Emergency Department Treatment of Migraine
Bibliographic record
Abstract
ABSTRACT Introduction A migraine treatment protocol implemented in the emergency department of an urban hospital allowed us to evaluate the effectiveness of propofol compared to metoclopramide as well as the safety of the protocol. Methods We reviewed the health records of all patients aged 16 years and older treated with propofol for migraine between May 2014 and August 2017 at a teaching hospital in Québec City (CHUL). The care protocol consisted of administering propofol (20 mg) every 5–10 minutes as needed (up to 6 doses), monitoring vital signs before and after each dose and continuous cardiac monitoring. The primary outcome measure was the mean reduction of pain following first-line therapy (propofol or metoclopramide). The secondary outcome measures were 1) adjusted relative risks of requiring rescue medication after first-line therapy; 2) incidence of the following side effects of propofol received as first or second-line therapy: low arterial pressure (< 90 systolic or < 65 mean), desaturation, excessive sedation, arrhythmia. The cohorts were paired for gender, age, triage priority, and month/year of ED visit. Results Files of 34 patients given propofol and 58 given metoclopramide as first-line treatment were analyzed. Five metoclopramide-treated patients received propofol as rescue medication. Among propofol-treated patients, 29.4% experienced pain relief compared to 66% in the metoclopramide group (p < 0.001). Rescue medication was more frequent in first-line propofol patients (82.4% versus 37.9%, p < 0.001). In this group, four participants (10.3%) received intravenous fluid bolus for mean blood pressure below 60, but no persistent desaturation, bradycardia, excessive sedation, or arrhythmia was recorded. Conclusion Though less effective than metoclopramide, propofol at low doses may be an alternative to treat migraine in the ED. Monitoring of vital signs (especially blood pressure) would be prudent but continuous nursing is likely unnecessary.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".