A practical guide for performing arthrography under fluoroscopic or ultrasound guidance
Bibliographic record
Abstract
Arthrography has been an essential technique in musculoskeletal radiology for more than 100 years now and remains useful in combination with computer tomography and magnetic resonance imaging for a detailed assessment of articular structures, or by itself as a way to confirm the adequate distribution of therapeutic injections [ 1 , 2 ]. More recently, modifications of the technique using alternative approaches such as those targeting the articular recesses [ 3 ], and/or ultrasound guidance have been published [ 4 , 5 ]. Targeting the articular recess instead of the radiological joint space (Fig. 1 ) is optimal when the latter is not accessible due to overlapping normal bone structures or severe degenerative changes such as osteophytes (Fig. 2 ), and may help to avoid patient manipulation and tube angulation [ 3 ]. With this technique, the needle is advanced until contact with bone, thus providing a depth limit to insertion and potentially increasing the safety of the procedure. Moreover, this approach can help avoid articular fibrocartilages (labra and menisci). Finally, this technique is transposable to ultrasound guidance where the needle is best placed tangentially to the transducer rather than vertically. Indeed, ultrasound guidance for performing arthrography is now favoured over fluoroscopy by many specialists [ 6 ]. The principal advantages are the absence of ionising radiation for the patient and the operator, the possibility of operating ultrasound equipment outside of a radiology department (office practice), and imaging of all the soft tissues surrounding the joint, leading to an accurate diagnosis prior to the therapeutic injection and avoidance of any critical structures in the path of the needle.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.148 | 0.167 |
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".