Bibliographic record
Abstract
The Musculoskeletal Tumor Society and American Academy of Orthopaedic Surgeons recently collaborated on a clinical practice guideline Use of Imaging Prior to Referral to an Orthopaedic Oncologist. The complete manuscript is available on OrthoGuidelines (www.orthoguidelines.org) and the Musculoskeletal Tumor Society website (www.msts.org). This clinical practice guideline is designed to assist practitioners without specialization in musculoskeletal tumors to determine the most efficacious imaging modalities for establishing an accurate diagnosis and treatment plan when confronted with a bone or soft-tissue lesion of unknown etiology. A panel of experts with interest and expertise in orthopaedic surgery, orthopaedic oncology, and musculoskeletal radiology created relevant questions and synthesized existing literature into 12 topics and 27 recommendations. The group considered several common clinical and radiographic features often seen at the initial presentation of a bone or soft-tissue tumor of the pelvis or extremities. This guideline is intended to inform front-line practitioners to give insight into imaging modalities that are of minimal clinical utility, as well as those that are critical to establishing an accurate diagnosis and assessing the urgency of referral to an oncologic specialist. In addition to a summary of the primary findings of the report, we included three cases that illustrate clinical scenarios in which the guidelines can assist in determining the most appropriate first-line management. The strengths of the relevant guidelines are noted.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.006 |
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".