Baker Kisti Tanı Ve Tedavisinde Ultrason Görüntülerinin Hastayla Paylaşılmasının
Bibliographic record
Abstract
Ultrasound (US) is widely used during musculoskeletal diagnostic and interventional procedures. It is doubtless that interventional US helps to reach the target precisely. The aim of this study was to explore whether the treatment outcome would be affected if the patients simultaneously follow the US screen. In this regard, we used the clinical scenario of Baker s cyst (BC) as a simple and comprehensive model. Totally, 52 adult patients who had symptomatic BC were recruited and the diagnosis was confirmed by using US. Then the patients were randomized into two groups: Group 1: not following the US screen; Group 2: following the US screen during the procedure. Symptoms of BC like swelling, pain and range of motion were assessed by using Rauschning-Lindgren Classification (RLC) from 0 to 3. Statistical significance was set at p 0.05. Both groups had similar initial Visual Analog Scale (VAS), WOMAC (Western Ontario and McMaster Universities Osteoarthritis Index), RLC scores and US measu
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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; both teacher heads agree on what is shown here.
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".