Faculty Opinions recommendation of Afferent drive elicits ongoing pain in a model of advanced osteoarthritis.
Bibliographic record
Abstract
Osteoarthritis (OA) is a chronic condition characterized by pain during joint movement.Additionally, patients with advanced disease experience pain at rest (i.e., ongoing pain)that is generally resistant to non-steroidal anti-inflammatory drugs (NSAIDs).Injection of monosodium iodoacetate (MIA) into the intra-articular space of the rodent knee is a well-established model of OA that elicits weight-bearing asymmetry and referred tactile and thermal hypersensitivity.Whether ongoing pain is present in this model is unknown.Additionally, the possible relationship of ongoing pain to MIA dose is not known.MIA produced weight asymmetry, joint osteolysis, and cartilage erosion across a range of doses (1, 3, and 4.8 mg).However, only rats treated with the highest dose of MIA showed conditioned place preference to a context paired with intraarticular lidocaine, indicating relief from ongoing pain.Diclofenac blocked the MIA-induced weight asymmetry but failed to block MIA-induced ongoing pain.Systemic AMG9810, a TRPV1 antagonist, effectively blocked thermal hypersensitivity, but failed to block high dose MIAinduced weight asymmetry or ongoing pain.Additionally, systemic or intra-articular HC030031, a TRPA1 antagonist, failed to block high dose MIA-induced weight asymmetry or ongoing pain.Our studies suggest that a high dose of intra-articular MIA induces ongoing pain originating from the site of injury that is dependent on afferent fiber activity but apparently independent of TRPV1 or TRPA1 activation.Identification of mechanisms driving ongoing pain may enable development of improved treatments for patients with severe OA pain and diminish the need for joint replacement surgery.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.579 | 0.310 |
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".