Preclinical Assessment of Inflammatory Pain
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
While acute inflammation is a natural physiological response to tissue injury or infection, chronic inflammation is maladaptive and engenders a considerable amount of adverse pain. The chemical mediators responsible for tissue inflammation act on nociceptive nerve endings to lower neuronal excitation threshold and sensitize afferent firing rate leading to the development of allodynia and hyperalgesia, respectively. Animal models have aided in our understanding of the pathophysiological mechanisms responsible for the generation of chronic inflammatory pain and allowed us to identify and validate numerous analgesic drug candidates. Here we review some of the commonly used models of skin, joint, and gut inflammatory pain along with their relative benefits and limitations. In addition, we describe and discuss several behavioral and electrophysiological approaches used to assess the inflammatory pain in these preclinical models. Despite significant advances having been made in this area, a gap still exists between fundamental research and the implementation of these findings into a clinical setting. As such we need to characterize inherent pathophysiological pathways and develop new endpoints in these animal models to improve their predictive value of human inflammatory diseases in order to design safer and more effective analgesics.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it