Bibliographic record
Abstract
BACKGROUND: The mechanisms subserving deep spinal pain have not been studied as well as those related to the skin and to deep pain in peripheral limb structures. The clinical phenomenology of deep spinal pain presents unique features which call for investigations which can explain these at a mechanistic level. METHODS: Targeted searches of the literature were conducted and the relevant materials reviewed for applicability to the thesis that deep spinal pain is distinctive from deep pain in the peripheral limb structures. Topics related to the neuroanatomy and neurophysiology of deep spinal pain were organized in a hierarchical format for content review. RESULTS: Since the 1980's the innervation characteristics of the spinal joints and deep muscles have been elucidated. Afferent connections subserving pain have been identified in a distinctive somatotopic organization within the spinal cord whereby afferents from deep spinal tissues terminate primarily in the lateral dorsal horn while those from deep peripheral tissues terminate primarily in the medial dorsal horn. Mechanisms underlying the clinical phenomena of referred pain from the spine, poor localization of spinal pain and chronicity of spine pain have emerged from the literature and are reviewed here, especially emphasizing the somatotopic organization and hyperconvergence of dorsal horn "low back (spinal) neurons". Taken together, these findings provide preliminary support for the hypothesis that deep spine pain is different from deep pain arising from peripheral limb structures. CONCLUSIONS: This thesis addressed the question "what is different about spine pain?" Neuroanatomic and neurophysiologic findings from studies in the last twenty years provide preliminary support for the thesis that deep spine pain is different from deep pain arising from peripheral limb structures.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".