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Record W3032368856 · doi:10.1089/acu.2020.1413

The Anatomical Relationship Between Acupoints of the Face and the Trigeminal Nerve

2020· article· en· W3032368856 on OpenAlexaff
Leah Meltz, Daniel Guillot Ortiz, Poney Chiang

Bibliographic record

VenueMedical Acupuncture · 2020
Typearticle
Languageen
FieldMedicine
TopicAcupuncture Treatment Research Studies
Canadian institutionsYork UniversityIntegrity Testing Laboratory (Canada)
Fundersnot available
KeywordsAcupunctureMedicineMoxibustionTrigeminal nerveCadaverDissection (medical)AnatomyTrunkFacial nerveTrigeminal neuralgiaSurgeryPathology

Abstract

fetched live from OpenAlex

Objective: Acupuncture continues to gain popularity as a first treatment option for a variety of conditions; however, an in-depth understanding of the relationships between the acupoints and the underlying anatomy of the human body is often unclear. This article updates the anatomical relationship between facial acupoints and the trigeminal nerve (CN V) and contrasts the results against the standard textbook Chinese Acupuncture and Moxibustion . Methods: A literature review, cadaver dissection, and a neuroanatomical stimulation of the CN V was conducted, focusing on the anatomical locations of the acupoints along the CN V on the face and nerve block targets. The results were contrasted against the standard acupoint location and nerve targets described in Chinese Acupuncture and Moxibustion . Results: The present article classifies CN V acupuncture targets according to 4 different types: (1) trunk; (2) bifurcation; (3) branch; and (4) anastomoses. The results of this exploration highlight the specificity with which acupoints are located in relation to the CN V. Areas of high nerve density correspond to several acupoints. Consequently, acupoints overlay closely with CN V branches as they emerge and bifurcate on the face. Conclusions: There is a clear and neuroanatomically relevant relationship between facial acupoints and the CN V.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.014
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.503
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.036
GPT teacher head0.324
Teacher spread0.288 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations17
Published2020
Admission routes1
Has abstractyes

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