Drug Disease Interactions: Role of Inflammatory Mediators in Pain and Variability in Analgesic Drug Response
Bibliographic record
Abstract
Pain has both physical and emotional components. Physical noxious stimuli activate peripheral sensory neurons that, in turn, relay signals to the spinal and supraspinal nuclei. Subsequently, these signals activate areas within the brain associated with pain. Despite considerable knowledge in this area, analgesics may provide pain complete relief in only one out of five patients. Failure to manage pain may be due to a lack of understanding of the neurobiological processing of pain. Factors such as anticipation, anxiety and pain history play roles in the perception of pain. Non-neuronal cells such as those of the immune system influence perception and modulation of pain by the nervous system. In post-dental surgery patients, the severity of the pain and the relief following administration of anti-inflammatory analgesics has been linked to the time course of inflammatory mediators. Similarly, the relief of post-operative pain after abdominal surgery is also associated with a reduction in expression of pro-inflammatory mediators. Administration of anti-cytokines to sciatica patients and subsequent pain relief further emphasizes the role of pro-inflammatory mediators in modulation of pain. Increased expression of inflammatory mediators may also alter response to analgesia. For example, rheumatoid patients with temporal mandibular joint disease with increased expression of interleukins prior to treatment demonstrate inadequate pain relief after administration of anti-TNF-. In addition, pain or its trauma impairs absorption of oral analgesics causing therapeutic failure. Improved analgesic pharmacotherapy may require a better understanding of the involvement of the inflammatory pathways.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".