Characteristics of coexisting patellofemoral joint osteoarthritis and tibiofemoral joint osteoarthritis in an Indonesian population: A cross-sectional study at a tertiary teaching hospital
Bibliographic record
Abstract
ABSTRACT Introduction: Historically, the diagnosis and treatment of osteoarthritis has been focused on the tibiofemoral joint solely. For the last two decades, the role of patellofemoral joint and its involvement on the degenerative joint disease has been investigated. To date, no data existed regarding patellofemoral osteoarthritis in our country, Indonesia. Methods: We performed a cross sectional study comprising of patients diagnosed with knee osteoarthritis in Fatmawati General Hospital, a tertiary teaching hospital in Jakarta, Indonesia. The subjects underwent knee radiograph from anteroposterior, lateral and skyline views. Results: A total of 66 subjects were included, 80% of the subjects were diagnosed with combined patellofemoral and tibiofibular joint osteoarthritis Kellgren-Lawrence grade III-IV. The Western Ontario and McMaster Universities Osteoarthritis Index score was measured 69.3 points, as this might be correlated with the advancement of the disease. Conclusions: Combined patellofemoral and tibiofemoral osteoarthritis constitutes a large portion of patients with knee osteoarthitis. Highlights:
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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".