Evaluation and treatment of spinal metastases: an overview
Bibliographic record
Abstract
Metastases to the spine are a common and somber manifestation of systemic neoplasia. The incidence of spinal metastases continues to increase, likely a result of increasing survival times for patients with cancer. Historically, surgery for spinal metastases has consisted of simple decompressive laminectomy. Results obtained in retrospective case series, however, have shown that this treatment provides little benefit to the patient. With the advent of better patient-related selection practices, in conjunction with new surgical techniques and improved postoperative care, the ability of surgical therapy to play an important and beneficial role in the multidisciplinary care of cancer patients with spinal disease has improved significantly. Controversy remains, however, with respect to the relative merits of surgery, radiotherapy, chemotherapy, or a combination of these treatments. In this topic review, the literature on spinal column and spinal cord metastases is collated to provide a description of the presentation, investigations, indications for surgical therapy, and the role of adjuvant cancer therapies for patients with spinal metastases. In addition, the authors discuss the different surgical strategies available in the armamentarium of the neurosurgeon treating patients with spinal metastasis.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".