Modality of hyperalgesia tested, not type of nerve damage, predicts pharmacological sensitivity in rat models of neuropathic pain
Bibliographic record
Abstract
Although many types of nerve damage can cause neuropathic pain, there are substantial commonalities in neuropathic pain symptoms, and patients can be divided into sub-groups based on their symptom profile rather than etiology. Mechanism-based treatment suggests that pharmacotherapy should be chosen be based shared commonalities of symptoms rather than etiology. The aim of the present study was to determine whether type of injury (etiology) or behavioral endpoint (symptom) is a better predictor of pharmacological responsivity in the most commonly used rodent models of neuropathic pain. We used the chronic constriction injury (CCI) model to directly compare the temporal and pharmacological characteristics of four different types of evoked stimuli; heat, pressure, acetone cooling and punctate mechanical. We then compared heat hyperalgesia and mechanical allodynia endpoints across etiologies using the spinal nerve ligation (SNL) model. Evoked pain responses in both models had strikingly different temporal characteristics. We then tested three standard therapies for neuropathic pain from different drug classes, oxycodone, gabapentin, and amitriptyline. Notably, regardless of the model tested, or the time of onset, common endpoints showed near-identical pharmacological responses, and not all endpoints were equally sensitive to drug intervention within one model. Hypersensitivity to heat and pressure were highly responsive to oxycodone, gabapentin, and amitriptyline; whereas cold and mechanical allodynia were more difficult to reverse. Moreover, CCI- and SNL-induced mechanical allodynia was completely insensitive to amitriptyline treatment. We conclude that regardless of model and time course of presentation, different symptoms of peripheral neuropathy have unique pharmacological responses.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".