A Qualitative Systematic Review of the Role of N-Methyl-d-Aspartate Receptor Antagonists in Preventive Analgesia
Bibliographic record
Abstract
UNLABELLED: We evaluated in a qualitative systematic review the effect of N-methyl-D-aspartate (NMDA) receptor antagonists on reducing postoperative pain and analgesic consumption beyond the clinical duration of action of the target drug (preventive analgesia). Randomized trials examining the use of an NMDA antagonist in the perioperative period were sought by using a MEDLINE (1966-2003) and EMBASE (1985-2003) search. Reference sections of relevant articles were reviewed, and additional articles were obtained if they evaluated postoperative analgesia after the administration of NMDA antagonists. The primary outcome was a reduction in pain, analgesic consumption, or both in a time period beyond five half-lives of the drug under examination. Secondary outcomes included time to first analgesic request and adverse effects. Forty articles met the inclusion criteria (24 ketamine, 12 dextromethorphan, and 4 magnesium). The evidence in favor of preventive analgesia was strongest in the case of dextromethorphan and ketamine, with 67% and 58%, respectively, of studies demonstrating a reduction in pain, analgesic consumption, or both beyond the clinical duration of action of the drug concerned. None of the four studies examining magnesium demonstrated preventive analgesia. IMPLICATIONS: We evaluated, in a qualitative systematic review, the effect of N-methyl D-aspartate antagonists on reducing postoperative pain and analgesic consumption beyond the clinical duration of action of the target drug (preventive analgesia). Dextromethorphan and ketamine were found to have significant immediate and preventive analgesic benefit in 67% and 58% of studies, respectively.
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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 | 0.068 | 0.195 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.019 | 0.017 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| 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".