Toward Standardized Ultrasound Measurements of Cartilage Thickness in Children: Figure 1.
Bibliographic record
Abstract
In the past decade, musculoskeletal ultrasound (US) has become well established as a diagnostic method in adult rheumatology. B-mode (or greyscale) US has been shown to be an excellent tool, equally as effective as magnetic resonance imaging (MRI), to assess joint effusions and synovial thickening1. Power Doppler US detects slow flow in small vessels, which is part of the pathological process in synovitis2. In addition, cartilage thickness can be assessed with US3. As one of the cardinal features of inflammatory arthritis is cartilage loss, and joint space narrowing is increasingly recognized as a factor in work disability and poor quality of life4, US might play an important role in the monitoring of patients with chronic arthritis. The clinical utility of musculoskeletal US is likely to be at least as important in pediatric rheumatology as it is in adult rheumatology. The longterm consequences of insufficiently treated and therefore persistently active juvenile arthritis are enormous given the young age of the patients5, and a recent review has outlined the impact on health related quality of life, physical function, and visual outcome6. The exact assessment of joint disease activity as well as the assessment of joint damage in the form of cartilage loss is therefore very important and has become ever more crucial with improvements in treatment. The induction of permanent remission is now possible for an increasing percentage of children but cannot always be reliably demonstrated on clinical examination alone7 … Address correspondence to Dr. Larché. E-mail: mlarche{at}mcmaster.ca
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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 | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.015 | 0.029 |
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".