Bibliographic record
Abstract
Along with increasing development and utilization of orthopedic reconstructive surgical techniques and the recognized value of cross-sectional imaging in the assessment of musculoskeletal pathology and orthopedic treatment planning, has come an increasing demand for accurate cross-sectional imaging of the orthopedic patient following surgical intervention. Such imaging serves an important role in the noninvasive assessment of the postoperative status of the surgical procedure performed as well as a critical role in the assessment of recurrent, residual, or new symptoms following surgery. Such symptoms may be related to failure or complication of the surgical procedure or related to other causes of symptoms anatomically localized in a nonspecific manner to the operative site. Meaningful evaluation of imaging findings in patients following orthopedic surgery has necessitated an increased understanding on the part of the interpreting radiologist, of the basic principles and mechanics of orthopedic procedures. While an extensive review of orthopedic surgical techniques is well beyond the scope of this issue of the journal, many of the basic and fundamental orthopedic surgical procedures and principles have been reviewed in the context of the focus of the articles of the journal. Also reviewed are a number of important imaging hardware advances and technical optimizations which may serve to improve the quality of cross-sectional imaging studies following orthopedic procedures and possible instrumentation. The potential clinical utility and value of advanced cross-sectional imaging in the assessment of the postoperative orthopedic patient is being assessed in a seemingly ever-evolving fashion in the current radiologic and orthopedic literature. The authors contributing to this issue focusing on advanced imaging of the postoperative orthopedic patient have each shared their expertise and knowledge in this area and I wish to thank each of them for their efforts and outstanding work. I would also like to thank Dr David Karasick and Dr Mark Schweitzer for allowing me the privilege of serving as a guest editor of the Seminars in Muscuoskeletal Radiology and for their help and encouragement in putting together this issue of the journal.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".