The Natural History of Knee Osteoarthritis: India-based Knee Osteoarthritis Evaluation (iKare): A Study Protocol
Bibliographic record
Abstract
METHODOLOGY: Multi-center, cross-sectional, observational study. STUDY CENTER(S): Multiple centers in India. NUMBER OF PARTICIPANTS: 1,000. PRIMARY RESEARCH OBJECTIVE: To characterize patients and treatment utilized for orthopedic patients presenting to both private and public hospital centers in India with knee pain and symptoms suggestive of knee arthritis. INCLUSION CRITERIA: All patients 18 years of age or older who present to a recruiting hospital for treatment of knee pain will be eligible for participation. The subjects must be able to understand and complete the questionnaire. EXCLUSION CRITERIA: Patients with total knee replacement, open wound or evidence of recent surgery, or with a current or a history of tumor and/or fracture in the tibial plateau, femoral condyle or patella, in the affected knee are not eligible. STUDY OUTCOMES: This study aims to characterize the following: general demographics of patients presenting with knee pain, severity of knee symptoms at time of presentation, severity of knee pathology at time of presentation, factors associated with the decision to seek medical care, previous treatments and health care contacts, planned treatment, and gaps in treatment perceived by the patient and treating surgeons.
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 | 0.009 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 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".