Convergence of cutaneous, musculoskeletal, dural and visceral afferents onto nociceptive neurons in the first cervical dorsal horn
Bibliographic record
Abstract
The convergence of cutaneous, musculoskeletal, dural and visceral afferents onto nociceptive neurons in the first cervical dorsal horn was investigated in urethane/chloralose-anesthetized rats. Electrical stimulation was applied to facial, neck, shoulder and forepaw skin, cornea (COR), dura, second cervical (C2) nerve, hypoglossal nerve, temporomandibular joint, masseter (MAS) muscle and superior laryngeal nerve. In addition, acetic acid was injected intraperitoneally and microinjection of glutamate was applied to the tongue, MAS muscle, splenius cervicis muscle, dura and intrapericardial area. A total of 52 nociceptive neurons classified as wide dynamic range (n = 28) or nociceptive-specific (n = 24) was studied. All nociceptive neurons received afferent input from the skin and at least one COR, musculoskeletal, dural or visceral afferent source in the trigeminal (V) or cervical area but input from afferent sources caudal to the C2 innervation territory was sparse. The proportion of neurons responding to COR, dural, C2 nerve, hypoglossal nerve, temporomandibular joint, MAS muscle and superior laryngeal nerve stimulations was 87, 54, 85, 52, 73, 64 and 31%, respectively. Electrical stimulation of all tested sites showed a double logarithmic stimulus-response relation, and cluster analysis of the excitability to COR, musculoskeletal, dural and visceral stimulations revealed two groups of neurons, one mainly containing wide dynamic range neurons and one mainly containing nociceptive-specific neurons. These findings indicate that afferent convergence in first cervical dorsal horn nociceptive neurons may be limited to the craniofacial area and that they may play an important role in the integration of craniofacial and upper cervical nociceptive inputs.
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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 | 0.000 |
| 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 | 0.000 |
| 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".