Bibliographic record
Abstract
Acupuncture, an integral part of traditional Chinese medicine, has been practiced for over 2,500 years. Dr. William Osler, a renowned physician in Canada and the United States during the late 1800s, was also an acupuncturist. He learned acupuncture from Dr. Sydney Ringer, a prominent British physiologist and medical doctor. Osler developed a method for treating lower back pain that focused on local acupoints, utilizing long hatpins. He advocated for acupuncture in his teaching for four decades and included it in his seminal textbook The Principles and Practice of Medicine: Designed for the Use of Practitioners and Students of Medicine . In 1879, Osler experienced a failed case in his acupuncture practice. A wealthy patient’s severe lower back pain was aggravated after treatment, leading to public criticism and professional humiliation. This incident caused some scholars to undervalue acupuncture and speculate that Osler ceased practicing it thereafter. The reasons for such painful experiences in acupuncture are generally attributed to four factors: The first is poor needle quality and size: The needles used by Osler were rigid and had a diameter of ∼1.00 mm, which is significantly larger than modern acupuncture needles. The second is inadequate needling technique: Proper training and precision are crucial for painless acupuncture. The third is improper needle placement: Inserting needles into subcutaneous vessel walls or skin follicles can cause discomfort. The fourth is patient-specific reactions: Conditions such as hyperalgesia may increase sensitivity, requiring the use of distal acupoints rather than local ones. To minimize pain and adverse effects, effective communication with patients is essential. It is worth noting that the rate of severe pain caused by acupuncture is only 0.007%, and the risk of other adverse effects is very low.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.010 |
| Insufficient payload (model declined to judge) | 0.011 | 0.005 |
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".