Low‐dose Ketamine For Acute Pain Control in the Emergency Department: A Systematic Review and Meta‐analysis
Bibliographic record
Abstract
OBJECTIVE: There has been increased interest in the use of low-dose ketamine (LDK) as an alternative analgesic for the management of acute pain in the emergency department (ED). The objective of this systematic review was to compare the analgesic effectiveness and safety profile of LDK and morphine for acute pain management in the ED. METHODS: Electronic searches of Medline and EMBASE were conducted and reference lists were hand-searched. Randomized controlled trials (RCTs) comparing LDK to morphine for acute pain control in the ED were included. Two reviewers independently screened abstracts, assessed quality of the studies, and extracted data. Data were pooled using random-effects models and reported as mean differences and risk ratios (RRs) with 95% confidence intervals (CIs). We used the Grading of Recommendations Assessment, Development and Evaluation approach to assess the certainty of the evidence. RESULTS: Eight RCTs were included with a total of 1,191 patients (LDK = 598, morphine = 593). There was no significant difference in reported mean pain scores between LDK and morphine within the first 60 minutes after analgesia administration and a slight difference in pain scores favoring morphine at 60 to 120 minutes. The need for rescue medication was also similar between groups (RR = 1.26, 95% CI = 0.50 to 3.16), as was the proportion of patients who experienced nausea (RR = 0.97, 95% CI = 0.63 to 1.49) and hypoxia (RR = 0.38, 95% CI = 0.10 to 1.41). All outcomes were judged to have low certainty in the evidence. CONCLUSION: Low-dose ketamine and morphine had similar analgesic effectiveness within 60 minutes of administration with comparable safety profiles, suggesting that LDK is an effective alternative analgesic for acute pain control in the ED.
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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 | 0.013 | 0.038 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.032 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".