Perceptual Motor Features of Expert Acupuncture Lifting-Thrusting Skills
Bibliographic record
Abstract
BACKGROUND: Little is known with regard to how acupuncture skills are optimally taught, learnt and evaluated despite clear evidence that technical skill acquisition is important to trainee success in health professions. OBJECTIVES: This study reports an investigation of the sensorimotor aspects of the acupuncture lifting-thrusting skill in order to highlight the important kinematic and kinetic features of the action. The study also explores the role of perceptual acuity in accurate acupuncture performance. METHODS: Twelve novice and 12 expert acupuncturists watched a standardised video demonstrating the mild reinforcing and reducing technique of lifting-thrusting on an acupuncture point and then performed 10 trials of the technique on an artificial skin pad mounted on a six-axis force transducer with an infrared light-emitting diode affixed to the index finger of their dominant hand. The force transducer measured the force applied by participants as they needled the acupuncture point while an optoelectric camera measured the position of the diode. Subsequently, the participants engaged in two tests of general perceptual acuity. RESULTS: Repeated measures analyses of variance indicated that experts are more consistent in their trial-by-trial amplitude (p=0.03) and lifting-thrusting velocity (p=0.029) than novices. Measures of perceptual acuity revealed no differences between novices and experts. CONCLUSIONS: Movement amplitude and velocity consistency are the action features of the mild reinforcing and reducing lifting-thrusting skill that differentiate the performances of experts from novices. The acquisition of acupuncture expertise is a function of extended practice rather than any inherent perceptual ability.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".