A scoping review on the surgical management of metastatic bone disease of the extremities
Bibliographic record
Abstract
BACKGROUND: Management of metastatic bone disease of the extremities (MBD-E) is challenging, and surgical directions pose significant implications for overall patient morbidity and mortality. Recent literature reviews on the surgical management of MBD-E present a paucity of high-level evidence and global inconsistencies in study design. In order to steer productive research, a scoping review was performed to map and assess critical knowledge gaps. METHODS: The Arksey and O'Malley framework for scoping studies was followed. A comprehensive literature search identified a large body of literature pertaining to the surgical management of MBD-E. Study data and meta-data was extracted and presented using descriptive analytics and a thematic framework. Literature gaps were identified and analyzed. RESULTS: Three hundred eighty five studies from 1969 to 2017 were included. Studies were categorized into 11 separate themes, with the majority (63%) falling into the "surgical fixation strategies" theme, followed by "complications" at 7% and "prognosis and survival" at 6.2%. Less than 3% of studies were categorized in "patient related outcomes" or "epidemiology" themes. 89% of studies were retrospective and only 6 studies were of level 1 or 2 evidence. We identified a temporal increase in publication by decade, and all studies published on interventional radiology techniques or economic analyses were published after 2007 or 2009, respectively. 64.9% of studies were published in Europe and 20.3% were published in North America. Average patient age was 62 (± 5.2 years), and breast was the most common primary tumour (28%), followed by lung (17%) and kidney (15%). In terms of surgical location, 75% of operations involved the femur, followed by the humerus at 22% and tibia at 3%. CONCLUSIONS: We present a descriptive overview of the current published literature on the surgical management of MBD-E. Critical knowledge gaps have been identified through the development of a thematic framework. Consolidation of literary gaps must involve bolstered efforts towards patient and family-engaged research initiatives and assessment of patient-related surgical outcomes. Multi-disciplinary engagement in developing prospective research will also help guide evidence-based personalized practice for these patients. By building on existing comprehensive patient databases and registries, knowledge on survival and prognostic parameters can be greatly improved.
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 | 0.020 | 0.089 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.007 |
| Bibliometrics | 0.030 | 0.025 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".