Bibliographic record
Abstract
Objectives This study reviewed recent clinical research trends regarding the effectiveness of herbal medicine treatments for knee osteoarthritis. Methods We reviewed 4 different online databases (PubMed, China National Knowledge Infrastructure [CNKI], National Digital Science Library [NDSL], Oriental Medicine Advanced Searching Integrated System [OASIS]) from January 1, 2015 to August 31, 2019. Results Thirty-two randomized controlled trial papers were selected in this review. Most of them were conducted during 12 weeks, used Western Ontario and Mcmaster Universities Arthritis Index. In 22 of the papers, the effectiveness in the intervention groups was significantly higher than that in the control groups statistically (p<0.05). 5 studies reported intervention group was not inferior to the control group. Conclusions Most of studies showed herbal medicine treatments were statistically effective to knee osteoarthritis. More scientific and systematic clinical studies should be actively conducted in the future, and the results of this study could be used as basic data in the future clinical studies on herbal medicine treatment for knee osteoarthritis. (J Korean Med Rehabil 2019;29(4):47-60)
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 | 0.010 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.018 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".