Effects of Sono-feedback during aspiration of Baker’s cysts: A controlled clinical trial
Bibliographic record
Abstract
OBJECTIVE: To determine whether (diagnostic and interventional) ultrasound imaging can be used to provide visual feedback affecting treatment outcome (pain and disability). DESIGN: Controlled clinical trial. SUBJECTS: A total of 52 patients with (ultrasonographically confirmed) symptomatic Baker's cysts were enrolled. METHODS: The cysts were drained under ultrasound guidance and, if necessary, corticosteroid injections were given on the follow-up visit. In group I (n = 26) the patients did not observe the procedures on the ultrasound (US) screen. In group II (n = 26) the US images/videos were shown and explained to the patients. The patients were included in one of the groups consecutively, unless they refused the protocol of that group. Treatment outcome was assessed via US measurements, aspirate volumes, visual analogue scale (VAS) (knee pain, procedure discomfort), Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC), Rauschning-Lindgren Classification (RLC), Kellgren-Lawrence grading scale, Hospital Anxiety and Depression Scale, and paracetamol intake. RESULTS: The 2 groups were similar regarding US measurements, aspirate volume and paracetamol use (p-values > 0.05). In both groups all VAS (p < 0.001) and WOMAC (p < 0.05) scores decreased after treatment. Although initial VAS and WOMAC scores were similar between the groups, all VAS/WOMAC scores, except VAS-2, WOMAC-2 pain, and WOMAC-3 stiffness, were significantly lower in group II (all p < 0.05). Initial RLC scores were similar between the groups; however, group II had significantly lower scores at visits 2 and 3. CONCLUSION: In patients with Baker's cysts (diagnostic/interventional) US imaging can be used as a simple means of visual biofeedback, favourably affecting the treatment outcome (pain and disability).
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".