Spine Oncology—Metastatic Spine Tumors
Bibliographic record
Abstract
Surgery for spinal metastases remains the mainstay treatment for pain, instability, and neurological deterioration due to tumor infiltration of the spine. However, several new therapies are emerging which may improve outcomes further, and in some cases even replace the need for surgery. We now have a better understanding of which factors influence survival and quality of life after surgery, and this underpins the development and application of new treatments, and assessment of outcome.Depending on genetic subtyping of tumors, novel immunotherapies and chemotherapies may be very effective in prolonging quality of life. New surgical techniques allow smaller, quicker, and safer operations with less blood loss, pain, and quicker recovery after surgery. Radiation treatments have also leapt forward with the greater accuracy and higher doses possible from intensity-modulated photon radiation, stereotactic body radiation treatment, proton beam therapy, or carbon ion treatment. Combined with more advanced materials for vertebral body stabilization, computer navigation systems, and robotics, more can be done at earlier or later stages of the spinal disease than was previously possible, resulting in more options and improved outcomes for patients.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".