퇴행성 무릎관절염 변증도구의 신뢰도 평가 및 무릎 ROM, VAS, WOMAC과의 상관관계에 대한 탐색적 연구
Bibliographic record
Abstract
Objectives To evaluate the pattern identification tool for knee osteoarthritis and to investigate the relationship between pattern identification tool and knee ROM (range of motion), VAS (visual analog scale) and WOMAC (Western Ontario & McMaster Universities osteoarthritis index). Methods We studied 50 patients who are diagnosed as degenerative osteoarthritis . With one patient dropping out, total of 49 patient went through pattern identification tool. Re-test of the pattern identification tool was implemented after about a week. The reliability was analyzed by calculating ICC (intraclass correlation coefficient). Also, reliability of each pattern identification score was calculated along with influence factor and correlation between knee ROM, VAS and WOMAC. Results Reliability of the pattern identification tool was evaluated as ‘poor agreement beyond chance’ with ICC value of 0.396. Reliablility of each pattern identification score was calculated and four out of five were measured ‘good’ with one ‘moderate’ reliability. Two of inter-item consistency were ‘good’, two were ‘acceptable’ and one was ‘questionable’. Correlation between each pattern identification and knee ROM showed ‘clear negative linear relationship’ with two patterns. VAS showed ‘clear positivie linear relationship’ with all five patterns. WOMAC showed ‘positivie linear relationship’ with all five patterns. Conclusions According to the results, pattern identification tool for knee osteoarthritis seems to have meaningful relationship with other agnedas. Also, further research is needed to develop the tool. (J Korean Med Rehabil 2019;29(3):113-128)
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.048 |
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; both teacher heads agree on what is shown here.
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".