Neuroanatomical Significance of Acupuncture Points TE1–TE10 Based on the <i>Systematic Classic</i>
Bibliographic record
Abstract
Background: Although acupuncture is a highly effective treatment for pain management, it suffers from lack of reproducibility of results. One of the many variables involved in achieving reproducible clinical results is the specificity of an anatomical structure being stimulated. Lack of target specificity obscures scientific understanding of how acupuncture works, leading to skepticism about the efficacy of acupuncture within the medical community and prejudice regarding the wisdom imparted through classical acupuncture texts. Objectives: The goals of this study were to test the hypothesis that classical acupoint locations described in the classics have a strong foundation in neuroanatomy and to promote a consensus among practitioners about neuroanatomy-based acupuncture. Methods: Acupoint locations for TE 1–TE 10 described in the Systematic Classic were transliterated, and each acupoint neuroanatomical target was determined by literature review, dissection, and/or electrical stimulation. An objective comparison was made between classical acupoint location and acupoint descriptions in a contemporary Chinese acupuncture textbook. Results: Classical acupoints TE 1–TE 10 had specific neuroanatomical targets. Neuroanatomical differences of the TE acupoints were found between classical and contemporary descriptions. Of the ten contemporary acupoints, 60% of targets were ambiguous (TE 1 and TE 5–TE 9) while 40% (TE 2–TE 4, and TE10) missed the corroborated neuroanatomical targets completely. Conclusions: This study demonstrates that each acupoint between TE1 and TE10 targets a distinct nerve and/or muscle enabling the achievement of feedback of highly distinct tissue stimulation, without any target redundancy. The transmission of erroneous and ambiguous anatomical targets found in contemporary texts highlights the urgency in advancing a science-based approach to study the neuroanatomical intents of other acupoints.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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 teacher head, 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".