Use of narcotic analgesics in the emergency department treatment of migraine headache
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
OBJECTIVE: Treatment of acute migraine headache with narcotics is potentially ineffective and may lead to abuse. The authors examined the treatment practice variation across five linked EDs in one Canadian center, focusing on the use of narcotic analgesics and factors associated with their use. METHODS: Five hundred acute migraine headache patient charts were randomly selected from five Canadian EDs. Charts underwent a structured review to determine medication use. Data were analyzed, comparing those who received narcotics as first-line treatment with those who did not, using chi(2) and t-tests and logistic regression. RESULTS: The majority of patients (59.6%) received narcotics as first-line treatment. Numerous factors were associated with first-line narcotic treatment. Having taken antiheadache medications prior to ED presentation (odds ratio [OR]: 2.63; 95% CI: 1.53, 4.51) and hospital of presentation being other than Hospital A (e.g., Hospital D, OR: 6.32; 95% CI: 2.76, 14.46) increased the odds of receiving first-line narcotics. Having received a more urgent triage score (OR: 0.4; 95% CI: 0.24, 0.65) or having a longer duration of headache (OR: 0.994; 95% CI: 0.99, 0.99) decreased the odds of receiving first-line narcotics. CONCLUSIONS: Acute migraine management in these EDs does not meet current consensus guidelines. Factors associated with narcotic use are predictable, and a concerted effort to replace narcotics with more evidence-based first-line treatments is needed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.002 | 0.001 |
| 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 | 0.000 |
| 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 it