Standardized Ear Acupuncture Nomenclature Utilizing Auricular Landmarks and 3-D Graphic Imaging for Delineating Different Auricular Zones
Bibliographic record
Abstract
Background: The World Health Organization (WHO) has actively sought to bring international standardization of the terminology used in acupuncture texts, training, and research. Objectives: The purpose of the present article is to delineate specific auricular landmarks that can be utilized to differentiate one auricular zone from another, thus facilitating international communication regarding the specific, somatotopic location of different parts of the body on the external ear. Methods: Two-dimensional ear diagrams and photographs of actual ears were utilized to develop a detailed, three dimensional (3-D) image of the external ear. These 3-D images were then used to show the specific locations of auricular landmarks and auricular zones in both the American and Chinese systems for representing the somatotopic locations of different body areas. Results: 3-D images of the auricle were created that were able to depict the specific locations of auricular landmarks and auricular zones in both the American and Chinese zone systems. Conclusions: Distinctive auricular landmarks were identified and depicted on two-dimensional and 3-D images of the external ear. These landmarks allowed the comparison of an American auricular zone system to a standardized Chinese auricular zone system.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".