Management of Long-Bone Metastases: A Surgical Perspective
Bibliographic record
Abstract
Bone metastases are frequently encountered in the management of cancer. The incidence of such events is increasing in the geriatric population and most often they can be managed conservatively. Surgery is sometimes unavoidable to address some of these lesions and will represent a significant challenge for both the patient and the medical team. Patients presenting with bone metastases are a heterogeneous group. Some present late in the course of their disease after failing all treatment modalities whereas others present without a known diagnosis of cancer. In addition, some metastatic cancers are responsive to treatment and prolonged survival may be expected (e.g., myeloma, breast prostate, kidney and thyroid) as compared with others where therapeutic options are limited with concomitant decrease in life expectancy (e.g., lung, bladder and pancreas). Patients will benefit if physicians can recognize lesions at risk of fracture or already fractured, and understand the advantages and the limitations of surgery. Patient selection and the type of procedure performed are of outmost importance. Chances for a satisfactory functional outcome and survival rates following surgery should be known in order to avoid unnecessary procedures that may be associated with significant rates of complication. Appropriate surgical intervention for bone metastases can provide the elderly cancer patient with meaningful palliation and contribute to their overall 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".