Bibliographic record
Abstract
Bone tumors may present as incidental findings, with pain or loss of function, or as fractures. There is a broad range of indications for transarterial embolization (TAE) in primary or metastatic bone tumors: to reduce operative hemorrhagic risks, to simplify or allow more definitive surgery, or in the context of pain palliation, fever, bleeding, or hypercalcemic and other rheological factors. Embolization may also increase tumor sensitivity to chemotherapy or radiation therapy. The procedure itself is often complex with significant risk to adjacent structures and is usually part of a wider treatment strategy. There are many options of embolic agent, techniques, and end points but all aim to devascularize the tumor. Catheter angiography at the time of TAE is used to determine the correct embolic agent and technique with care taken to isolate at risk structures. Many factors determine the best choice of embolic material, probably the most important of which is operator experience. In life-threatening situations or in preoperative embolizations of metastatic tumors, many operators opt for a combination of particulate emboli and stainless steel or platinum coils. Agents discussed include polyvinyl alcohol particles, trisacryl microspheres, gelatin sponge, liquid embolic agents, and embolization coils. Tumor types treated include vascular metastatic lesions, commonly renal cell or thyroid, particularly in locations prone to fracture; giant cell tumors; aneurysmal bone cysts; vertebral hemangiomas, osteosarcomas; arteriovenous malformations; and osteoblastomas. TAE should be considered in the treatment algorithm of primary or secondary bone tumors. Specific benefit is present where there is a high risk of bleeding at surgery, where there is spinal involvement and neural encroachment, where active bleeding is present or in awkward surgical locations where prolonged surgery is anticipated.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".