Measurement Reliability for Acupoint Activity Determined with the Prognos Ohmmeter
Bibliographic record
Abstract
The concepts of meridians and acupoints are critical to traditional Chinese medicine but are met with skepticism in Western medicine. Empirical validation of these concepts is in its beginning stages and still hampered by problems with measurement. A promising avenue and foundation for validity testing is the demonstration that acupoint activity can be reliably measured via determination of electrical resistance at well-defined body surface points. In this article, efforts are described to maximize measurement reliability; we tested a variety of protocols to determine which method of data aggregation is associated with maximal reliability. Twenty-one healthy individuals were subjected to 5 repeated measurement cycles to test the predicted increase in reliability with increasing number of aggregated measurements. Reliability, defined as internal consistency, was indeed highest for 5 measurements (mean alpha = .88). Even the aggregate of only three measures was quite reliable (alpha = .84). Reliability for measuring acupoints on the left side of the body was roughly .05 higher than on the right side. Consistent with previous literature, we conclude that with repeated measures the reliability of electrical resistance measurements at acupoints is high and that a strong foundation for validation research is now laid.
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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 | 0.013 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".