Neuronal Plasticity Associated with Burn Injury and Its Relevance for Perception and Management of Pain in Burn Patients
Bibliographic record
Abstract
Through the introduction of the gate control theory and various subsequent works, Ronald Melzack has inspired many investigators worldwide to realize two important facts about pain. First, incoming pain messages are subject to both negative and positive modulation, which significantly affect its perception. Second, the progression of knowledge about the basic mechanisms underlying persistent and chronic pain is critically dependent on the increased understanding of the complexity of the symptoms experienced by pain patients. The present paper examines these two very important issues in an effort to understand better the mechanisms that underlie the pain suffered by burn patients. The physiological responses to burn injury involve many different mediators and mechanisms, all of which contribute to pain perception and development of neuronal plasticity underlying short and long term changes in pain sensitivity. While experimental burn injuries in humans and animals are typically well controlled and mild, in burn victims, the severity is much more variable, and clinical care involves repeated traumas and manipulations of the injured sites. Recurrent inputs from damaged and redamaged tissue impinge on a nervous system that becomes an active participant in the initiation of changes in sensory perception and maintenance of long term sensory disturbances. Recently acquired experimental evidence on postburn hyperalgesia, central hyperexcitability and changes in opioid sensitivity provides strong support that burn patients need an analgesic approach aimed at preventing or reducing the ′neural′ memory of pain, including the use of more than one treatment modality. Burn injuries offer a unique opportunity to combine experimental and clinical research to understand pain mechanisms better. Over the years, Ronald Melzack has insisted that one of the most laudable enterprises in research is to span the gap between these two often separate worlds.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".