HUBUNGAN OBESITAS TERHADAP DERAJAT NYERI PADA PASIEN LANSIA DENGAN SIMTOM OSTEOARTHRITIS DI POSYANDU LANSIA PUSKESMAS KAMPUNG BARU MEDAN MAIMUN TAHUN 2018
Bibliographic record
Abstract
Geriatric patients have multipatological problems, which are more related to chronic degenerative diseases such asosteoarthritis (OA), OA which is associated with an imbalance between damage and repair of joint cartilage and associatedsuch as obesity, weight lifting, trauma, and predisposition genetic. OA in a person can overcome severity Both in clinicalterms such as frills that can use Visual Analogue Scale (VAS) or Western Ontario and McMaster University OsteoarthritisIndex (WOMAC). This research is descriptive analytic using cross sectional design (adding latitude), a study sample OA of37 elderly patients at on elderly posyandu at Kampong Baru Health Center in January to September 2018 that was approvedaccording to inclusion and exclusion criteria. The sampling technique uses the Purposive sampling method, with dataanalysis using the chi-square test. Obesity to the degree of risk of elderly patients with osteoarthritis symptoms obtainedrelated to a significant relationship (p <0.05). Related to the significant relationship between obesity to the elderly in elderlypatients with osteoarthritis symptoms at maternal health services kampung baru medan.
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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.014 | 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".