CIFKAS A Measurer of Functional Disability Status in Knee Osteoarthritis
Bibliographic record
Abstract
Knee osteoarthritis (OA) results in structural and functional abnormalities and reduced functional performance abilities. In developing countries majority of population lives in rural areas having limited resources and socio-cultural biodiversity. Their personal, socio-cultural and occupational habits vary and need to be addressed. So a culturally relevant and contextually appropriate, Composite Indian Functional Knee Assessment Scale (CIFKAS) for measuring the functional status in knee osteoarthritis was formulated. 128 participants from various geographical regions of India of age range 40 to 60 years using convenient sampling were included and informed consent signed by the participants. Each participant was assigned to one of the two groups. 39 participants in group A reported no episode of knee pain while 89 participants in group B reported at least one episode of knee pain in the last two months. Each participant was assessed on Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) and CIFKAS and statistical analysis was done. The Pearson correlation coefficient calculated for all 128 subjects for pain, physical functional abilities and total functional disability score were 0.878, 0.925 and 0.945 respectively. Between group analysis was done using Independent t test and p value was found to be not significant for pain (<.178), highly significant for physical functional abilities (p<.0001) and very significant for total functional disability status score (p<.004). The results indicate that both WOMAC and CIFKAS are highly correlated and there is no difference between the two for measuring pain, but for functional ability and overall functional disability status within their functional context, CIFKAS is a better tool than WOMAC.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".