Bibliographic record
Abstract
Background: Acupuncture skeptics consider The Yellow Emperor's Internal Medicine—a classic that is important to the theory and practice of acupuncture—as a work based on metaphysics. This claim is one of the factors preventing the mainstream biomedical community from accepting the clinical merit of acupuncture. Objective: The goal of this research was to test the hypothesis that needle theory and acupoint locations described in the classics are based on anatomy and physiology, not on metaphysics. Methods: Passages from the Ling Shu (Spiritual Pivot) relevant to the properties of acupoints were translated and then interpreted using biomedical science. Acupoint locations described in the Jia Yi Jing (Systematic Classic) were transliterated, and their anatomical significance was determined by literature review, dissection, and/or electrostimulation. Finally, acupoint anatomy was compared to descriptions in a traditional Chinese acupuncture textbook. Results: Biomedical interpretation of the Ling Shu revealed a detailed appreciation of the peripheral nervous system, including observations, such as nerve depolarization, mechanical transduction, antidromic/orthodromic conduction, and segmental organization of the nervous system. Using the 11 acupoints of the Lung meridian as an illustration, 4 acupoints (LU 1, LU 2, LU 6, and LU 10) missed the verified neuroanatomical target, 5 acupoints (LU 3, LU 4, LU 7, LU 8, and LU 9) lacked anatomical precision, and only 2 acupoints (LU 5, LU 11) were neuroanatomically precise, compared to textbook descriptions. Conclusions: This study demonstrated that acupuncture classics have a biomedical basis and that each acupoint has distinct neuroanatomical specificity. Understanding precise acupoint anatomy has direct clinical advantages in verifying acupoint locations and evoking the correct De Qi response for each acupoint.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".