Relationship between radiologic gravity and predicting instruments of physical and mental health in elderly with knee osteoarthritis
Bibliographic record
Abstract
Abstract BACKGROUND: This study aimed to investigate the relationship between radiologic severity by the grades of the Kellgren-Lawrence scale (K&Ls) independently and in two groups (grades "0 and 1" and "2 to 4") and instruments that assess depression symptoms, cognitive loss, risk of fall and quality of life related to knee osteoarthritis. METHODS: The analyzed materials were derived from a database and collected between 2013-2014 in Amparo (São Paulo, Brazil). 181 elderly with knee osteoarthritis who had a radiologic exam were evaluated for depressive symptoms, cognitive loss, quality of life and risk of fall by: Geriatric Depression Scale (GDS), Mini Mental State Examination (MMSE), WOMAC (Western Ontario and McMaster Universities Osteoarthritis Index), Timed Up and Go test (TUG) and Berg Balance Scale (BBS). To statistical analyses was used Fisher's exact test, Mann-Whitney test, Kruskal-Wallis test and Spearman's coefficient. RESULTS: There was no significant relationship between the instruments investigated and the grades assessed individually. However, when assessed by groups, grades “2 to 4” had the worst WOMAC score, the highest frequency and the worst risk of fall in the BBS, but not in the TUG. For GDS and MMSE, no significant relationships were found. In addition, K&Ls was correlated with the WOMAC socre, with no differences between their domains. CONCLUSION: Only when evaluated in groups, the radiological scores of the Kellgren-Lawrence scale pointed to a worse status in the WOMAC and BBS and the WOMAC score accompanies the increase in the radiological grade.
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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 | 0.001 | 0.004 |
| 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 | 0.000 |
| 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".