Can radiographic patellofemoral osteoarthritis be diagnosed using clinical assessments?
Bibliographic record
Abstract
INTRODUCTION: The aim of this study was to determine whether participant characteristics and clinical assessments could identify radiographic osteoarthritis (OA) in individuals with clinically diagnosed, symptomatic patellofemoral osteoarthritis (PFOA). METHODS: Participant characteristics and clinical assessments were obtained from 179 individuals aged 50 years and over with clinically diagnosed symptomatic PFOA, who were enrolled in a randomised trial. Anteroposterior, lateral, and skyline X-rays were taken of the symptomatic knee. The presence of radiographic PFOA was defined as "no or early PFOA" (Kellgren and Lawrence [KL] grade ≤1 in the PF compartment) or "definite PFOA" (KL grade ≥2). Diagnostic test statistics were applied to ascertain which participant characteristics and clinical assessments could identify the presence of definite radiographic PFOA. RESULTS: ), longer pain duration (>2.75 years), higher maximum knee pain during stair ambulation (>47/100 mm), and fewer repeated single step-ups to pain onset (<21) were associated with the presence of definite radiographic PFOA. Multivariate logistic regression indicated that BMI, pain duration, and repeated single step-ups to pain onset were independently associated with radiographic PFOA and identified the presence of definite radiographic PFOA with an overall accuracy of 73%. CONCLUSION: In individuals over 50 years of age with a clinical diagnosis of PFOA, higher BMI, longer pain duration, and fewer repeated single step-ups to pain onset increased the likelihood of radiographic PFOA. However, overall diagnostic accuracy was modest, suggesting that radiographic PFOA cannot be confidently identified using these tests.
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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 | 0.007 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".