Effects of combining opioids and clinically available NMDA receptor antagonists in the treatment of pain.
Bibliographic record
Abstract
This thesis concerns the effects of combining opioids with clinically available NMDA receptor antagonists in the treatment of acute and chronic pain. There are a number of problems with the use of opioids, such as, the development of tolerance/hyperalgesia, the reduced effectiveness in (central) neuropathic pain, and troublesome adverse effects. These problems might be resolved by the combined use of opioids and clinically available drugs with N-methyl-D-aspartate (NMDA) receptor antagonist properties. In this thesis the effects of three NMDA-receptor antagonists were studied: ketamine (the racemic form and its s(+)-enantiomer), amantadine and methadone.From two clinical, perioperative studies it was concluded that the concept of pre-emptive analgesia (by using the opioid fentanyl plus low dose (racemic) ketamine before versus after incision) is not a useful clinical approach, but that a preventive approach (by using s(+)-ketamine started before induction of anesthesia with continuation into the postoperative period) might be a more clinically useful strategy. The results from a pilot study showed that the preventive use of amantadine was associated with lower postoperative morphine requirements in patients after radical prostatectomy. An animal study revealed that the combined use of amantadine and morphine resulted in synergistic effects in the formalin test in rats. For chronic pain patients it was found that the parenteral administration of low dose ketamine is an easily available and minimally invasive alternative for the treatment of neuropathic pain in cancer patients, and that oral methadone can be effective in reducing phantom limb pain intensity in humans.The combined use of opioids and NMDA receptor antagonists can reveal an opioid sparing effect in acute pain and can help in relieving neuropathic pain. The future clinical benefits of these effects remain to be determined.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".