Kajian Teknologi Kesehatan atas Perbedaan Efek Analgesia dari Elektroakupunktur dengan Frekuensi Rendah, Kombinasi, dan Tinggi, pada Nyeri Punggung Bawah
Bibliographic record
Abstract
Low back pain (LBP) was an important public health problem which ranked second after upper respiratory infectionin adults. WHO has recommended acupuncture for pain treatment. However, there was a lack of study that show the optimal frequency of electroacupuncture for pain treatment. This study aimed to determine the optimal frequency of electroacupuncture for the treatment of low back pain. This study was a double blind randomized controlled trial. A sampleof 40 subjects was selected at random of all (60) patients visiting the acupuncture clinic at Puskesmas Sragen Kota from September to Desember 2007. The subjects consist of 10 control subjects (paracetamol), 10 electroacupuncture subjects with low frequency (2Hz), 10 subjects with combined frequency (20/50Hz), and 10 subjects with high frequency (100Hz). The electroacupuncture was administered for 7 times. Pain was measured twice, before and after the treatment, using of McGill Pain Questionnaire. The data was analyzed with F test (ANOVA) and Post Hoc Test, which was run on SPSS v.15 program. Results of the study showed statistically significant different in the reduction of pain before and after the electroacupuncture treatment (F = 6.60; p = 0.001). There was a statistically significant different in pain reduction between control and low frequency (score difference -10.4; p = 0.032), control and combined frequency (score difference -1 2.1;p = 0.015), and control and high frequency as well (score difference -16.1; p = 0.004). Difference in the pain reduction was statistically non-significant between low and combined frequencies (score difference -1. 7; p = 0. 999), between combined and high frequencies (score difference -4.0; p = 0.928), between low and high frequencies as well (score difference -5. 7;p = 0. 726). This study concluded that the lowest frequency of electroacupuncture was the most optimal for pain reduction.Therefore, this study recommended the use of low frequency of 2 Hz for treating low back pain by electroacupuncture.
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".