Longitudinal survival trends of patients with cancer with surgically managed appendicular metastatic bone disease: systematic review
Bibliographic record
Abstract
BACKGROUND: Advances in systemic cancer therapies have improved survival for patients with metastatic carcinoma; however, it is unknown whether these advances have translated to improved survival for patients with appendicular metastatic bone disease (A-MBD) after orthopedic interventions. We conducted a study to evaluate the trend in overall survival for patients who underwent orthopedic surgery for A-MBD between 1968 and 2018. METHODS: A systematic search of Embase and Medline to identify studies published since 1968 evaluating patients treated with orthopedic surgery for A-MBD was conducted for a previously published scoping review. We used a meta-regression model to assess the longitudinal trends in 1-, 2- and 5-year overall survival between 1968 and 2018. The midpoint year of patient inclusion for each study was used for analysis. We categorized primary tumour types into a tumour severity score according to prognosis for a further meta-regression analysis. RESULTS: Of the 5747 studies identified, 103 were retained for analysis. Meta-regression analysis showed no significant effect of midpoint study year on survival across all time points. There was no effect of the weighted average of tumour severity scores for each study on 1-year survival over time. CONCLUSION: There was no significant improvement in overall survival between 1968 and 2018 for patients with A-MBD who underwent orthopedic surgery. Orthopedic intervention remains a poor prognostic variable for patients with MBD. This finding highlights the need for improved collection of prospective data in this population to identify patients with favourable survival outcomes who may benefit from personalized oncologic surgical interventions.
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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 | 0.006 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.012 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".