미세전류치료와 초음파치료가 슬관절전치환술 후 통증경감과 기능회복에 미치는 영향
Bibliographic record
Abstract
Purpose: The purpose of this study was to investigate pain relief and functional recovery after total knee replacement. Methods: The treatment was performed by dividing individuals into a control group (n1=5), ultrasound treatment group (n2=5), and micro-current treatment group (n3=5). The control group applied the hot pack for 15 minutes, Transcutaneous Electrical Nerve Stimulation (TENS) for 15 minutes, and Continuous Passive Movement (CPM) for 40 minutes. The ultrasound therapy group applied the frequency of 1 MHz, intensity of 1.0 W/㎠ for five minutes following the same treatment as the control group. The micro-current therapy group applied the intensity of 25 mA, and pulsation frequency 5 pps for 15 minutes following the same treatment as the control group. After treatment, Visual Analogue Scale (VAS), Korean Western Ontario and McMaster Universities Arthritis Index (K-WOMAC), Berg Balance Scale (BBS), Range of Movement (ROM) and wound length was measured. Results: VAS showed significant effect in the control group and micro-current therapy group during the treatment period. According to the treatment of K-WOMACK, BBS, ROM, and Healing wounds showed main effects between groups. Conclusion: According to the results of this study, data showed improvement of pain relief, wound healing effects, and range of motion recovery. Thus, these selected treatments were effective after total knee replacement. In other words, electrical treatment continues to influence pain relief and functional recovery after total knee replacement.
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 | 0.000 | 0.000 |
| 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) | 0.002 | 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".