Advances in personalized treatment of metastatic spine disease
Bibliographic record
Abstract
The spine is one of the most common sites of bony metastases, and its involvement leads to significant patient morbidity. Surgical management in these patients is aimed at improving quality of life and functional status throughout the course of the disease. Resection of metastases often leads to critical size bone defects, presenting a challenge to achieving adequate bone regeneration to fill the void. Current treatment options for repairing these defects are bone grafting and commercial bone cements; however, each has associated limitations. Additionally, tumor recurrence and tumor-induced bone loss make bone regeneration particularly difficult. Systemic therapeutic delivery, such as bisphosphonates, have become standard of care to combat bone loss despite unfavorable systemic side-effects and lack of local efficacy. Developments from tissue engineering have introduced novel materials with osteoinductive and osteoconductive properties which also act as structural support scaffolds for bone regeneration. These new materials can also act as a therapeutic reservoir to sustainably release drugs locally as an alternative to systemic therapy. In this review, we outline recent advancements in tissue engineering and the role of translational research in developing implants that can fully repair bone defects while also delivering local therapeutics to curb tumor recurrence and improve patient quality of life.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".