Variations and Improvement Measures of TCM Clinical Pathway for Knee Osteoarthritis
Bibliographic record
Abstract
Objective To research the therapeutic effect and variation causes of TCM clinical pathway for knee osteoarthritis(KOA) and propose measures for improvement. Methods Sixty cases of KOA were treated with TCM clinical pathway. The scores of visual analogue scale( VAS),Lequesne index and Western Ontario and McMaster Universities Arthritis Index(WOMAC) were compared before and after treatment. The changes were closely observed according to the contents of TCM clinical pathway table. The reasons for variation were described in detail and the improvement measures were proposed. Results The total effective rate of TCM clinical pathway for KOA was 98. 3%. The scores of VAS,Lequesne index and WOMAC were significantly decreased after treatment(P 0. 05 or P 0. 01). The positive variation was 11. 21% and the negative variation was 88. 79%. The sources of variation showed that medical personnel accounted for 28. 97%,patients accounted for 32. 71%,hospital systems accounted for 31. 78% and disease outcome accounted for 6. 54%. The controllable variation was 50. 47% and the medical personnel and hospital systems accounted for 28. 97% and 21. 50% respectively. The uncontrollable variation was 49. 53% and the patients,hospital systems and disease outcome accounted for 32. 71%,10. 28% and 6. 54% respectively. Conclusion The TCM clinical pathway for KOA is effective and it can obviously relieve syndrome. The reasons for variation are medical personnel,patients,hospital systems and disease outcome. The improvement measures are strengthening cooperation of hospital departments,training medical personnel,publicizing TCM clinical pathways and advocating rational medical therapy.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".