EULAR standardised training model for ultrasound-guided, minimally invasive synovial tissue biopsy procedures in large and small joints
Bibliographic record
Abstract
OBJECTIVES: To develop an EULAR training model for education in synovial tissue biopsy (STB) under ultrasound guidance (UG) following a stepwise approach: (1) development of educational material on UGSTB in large and small joints; (2) assessment of the validity, reliability and feasibility of the UGSTB educational procedure on cadaveric specimens; (3) validation of this procedure in live patients. METHODS: Using a nominal group (NG) and a DELPHI consensus methodology, educational audio-visual (AV) material and minimal requirements for education in UGSTB were developed by an expert panel. Then the experts performed an UGSTB on cadaveric joints using the developed approach. The samples retrieved from the cadaveric joints were confirmed histologically and the procedure was then tested by a group of ultrasonographers with different expertise for feasibility and face validity. The AV material and the practical procedures' phases were subsequently ranked by the experts to finalise the training model for performing UGSTB in patients. The ST retrieved in patients was assessed for tissue quality. RESULTS: Based on NG and DELPHI processes, educational material and a stepwise standardised cadaver-based training model were developed. The knee was the cadaveric joint with the highest yield of histologically good quality of ST. 90% of the UGSTB from patients showed synovial membrane and 77% intact lining layer. CONCLUSIONS: This EULAR endeavour provided a consensus-based comprehensive educational material and a practical cadaver-based model for training in UGSTB, which has shown feasibility and validity in tissue acquisition in specimens and patients.
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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 | 0.042 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".