Detection of Pain Severity with the Full Cup Test in Knee Osteoarthritis and Its Relationship with Knee Function and Quality of Life
Bibliographic record
Abstract
Knee osteoarthritis (OA) is a degenerative joint disease characterized by subchondral sclerosis, cartilage erosion, osteophyte formation, and both biochemical and morphological alterations in the synovial membrane of the articular cartilage.1 It is the most prevalent joint disorder globally, affecting approximately 302 million individuals, and is a leading cause of physical disability, particularly among older adults. 2 The incidence and prevalence of chronic OA are steadily increasing in parallel with global population aging, making it a significant public health concern.3 Additionally, around 25% of individuals over the age of 55 report at least one episode per year, and approximately 13% of elderly individuals have been diagnosed with knee OA for over seven years. 4 Risk factors for knee OA include advancing age, female sex, genetic predisposition, obesity, elevated bone mineral density, previous trauma, physical Cite this article as: Demir Karakılıç G, Şahingöz Bakırcı E. Detection of pain severity with the full cup test in knee osteoarthritis and its relationship with knee function and quality of life.Arch Basic Clin Res.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".