Prognostic Factors Associated With Bone Lymphoma Primarily Presenting in the Spine
Bibliographic record
Abstract
STUDY DESIGN: Retrospective analysis. OBJECTIVE: To determine the prognostic indicators in patients with bone lymphoma of the spine. SUMMARY OF BACKGROUND DATA: To date, prognosis for patients with spinal bone lymphoma is based on results from small series. Large population-based observational studies are lacking. METHODS: The Surveillance, Epidemiology, and End Results Registry was used to identify all patients with bone lymphoma of the spine from 1995 through 2014. Overall survival (OS) and disease-specific survival (DSS) estimates were obtained using the Kaplan-Meier method and compared across groups using log-rank test. Association of survival with variables was assessed using Cox proportional-hazards regression analysis. Nomograms were established and validated by R software. RESULTS: A total of 1338 patients were identified with bone lymphoma of the spine. The trend of incidence was relative steady from 1995 to 2014. The mean and median age of diagnosis was 62.9 and 66.0 years, respectively. Men constituted a slightly greater proportion (55.5%) of the cohort. The most common subtype of lymphoma was diffuse large B-cell lymphoma (59.8%). The 5- and 10-year OS rates for those patients were 60.6% and 44.7%, respectively. Multivariate analysis revealed that age, sex, marital status, and race were sociodemographic predictors of OS and DSS. Extracompartmental invasion of tumor and stage IV of lymphoma were significantly correlated with poor OS and DSS. Patients without surgery or radiotherapy was linked to a poor OS. Both OS and DSS of patients with spinal bone lymphoma improved in 2005 to 2014 when compared to 1995 to 2004. In addition, pretreatment nomograms to predict 5- and 10-year survival were established. CONCLUSION: The findings of this study provide population-based estimates of the prognosis for patients with spinal bone lymphoma. Both sociodemographic and clinical factors may have an impact on the outcomes. LEVEL OF EVIDENCE: 4.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".