Ketamine for Adults with Severe Asthma Exacerbation: A Systematic Review and Meta-analysis
Bibliographic record
Abstract
BACKGROUND: Severe asthma mandates careful attention and timely management, and the benefit of ketamine in severe asthma exacerbations in adult patients require further exploration. METHODS: We conducted a systematic review and meta-analysis of the use of ketamine in cases of acute asthma exacerbation in adults. We searched PubMed, Google Scholar, Cochrane databases, and gray literature (ClinicalTrials.gov and World Health Organization International Clinical Trials Registry Platform); we also searched the reference lists of included articles and any systematic reviews and meta-analyses identified therein. Our search covered the period from 1963 to August 20, 2021. Search terms were “ketamine” AND “asthma”. RESULTS: Of 25 540 articles, two studies were included in the analysis. The total number of patients included in the studies was 136 (68 in the ketamine groups and 68 in the placebo group). The pooled effect size was 0.30 (95% CI: -0.04, 0.63) favouring ketamine over placebo, p=0.08, (I2=0%, p=0.39). A paired t-test revealed that ketamine improved the mean peak expiratory flow rate (PEFR) from 242.4 (SD=146.23) to 286.95 (SD=182.22), p=0.33, representing an 18.38% improvement. CONCLUSION: Ketamine can induce a 30% improvement in PEFR, representing a small positive effect in the treatment of acute severe asthma exacerbation in the emergency department (ED). The improvement was not statistically significant; nonetheless, since the improvement could be as great as 63% versus only a 4% possibility of no benefit/harm, the benefit appears to considerably outweigh any harm.
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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 | 0.010 | 0.025 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.035 |
| Bibliometrics | 0.005 | 0.006 |
| 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) | 0.003 | 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".