Hubungan Antara Kualitas Nyeri Dengan Kemampuan Aktivitas Fungsional Penderita Osteoartritis Lutut
Bibliographic record
Abstract
Background: Osteoarthritis also called degenerative joint disease is an abnormality on cartilage (joint cartilage) is characterized by changes inclinical, histologist, and radiology. Namely the ability offunctional activity of the patient's ability to perform daily activities, limitations on patient activity due to the pain. \nObjective:To determine the relationship between the quality of pain with functional activity capability knee osteoarthritis sufferers. \nMethods: This study used epidemiological study design cross sectional analytic approach to determine the relationship between the quality of pain with the ability of functional activity. The number of samples was 34 respondents knee osteoarthritis who meet inclusion and exclusion criteria, obtained from non-random sampling with purposive random sampling technique. Data obtained from the questionnaires Visual Analogue Scale (VAS) and the Western Ontario and McMaster Universities OsteoarthritisIndex (WOMAC). \nResults: To test the hypothes isused Spearman test. Obtained strong correlation values between variables (r =0.510) and asignificant value of 0.002 (p <0.05). \nConclusion: There is a relationship between the quality of pain with the ability of the functional activity of patients with osteoarthritis of the knee, that is, if an increase in the value of the quality ofthe pain will be followed by an increase in daily activities disabilities.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 0.004 |
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".