Optimizing the Adverse Event and HRQOL Profiles in the Management of Primary Spine Tumors
Bibliographic record
Abstract
STUDY DESIGN: Systematic literature review. OBJECTIVE: To investigate if evidence-based principles of oncologic resection for primary spinal tumors are correlated with an acceptable morbidity and mortality profile and satisfactory health-related quality of life (HRQOL) measures. SUMMARY OF BACKGROUND DATA: Respecting oncologic principles for primary spinal tumor surgery is correlated with lower recurrence rates. These interventions are, however, often highly morbid. METHODS: A systematic literature review was performed to address the objectives by searching MEDLINE and EBMR databases. Articles that met our inclusion criteria were reviewed. GRADE guidelines were used for recommendation formulation. RESULTS: A total of 25 articles addressing the morbidity and mortality profile of primary spinal tumor surgery were identified. For sacral tumors, complication rates of up to 100% have been reported and complication-related death ranged from 0% to 27%. Mobile spine tumor complication rates varied from 13% to 73.7% and complication-related death ranged from 0% to 7.7%. Seven articles examining HRQOL for this patient population were identified. The limited literature showed comparable patient HRQOL profiles to those with benign conditions such as degenerative disc disease. CONCLUSION: Respecting oncologic principles for primary spinal tumors are correlated with high adverse event rates. We recommend that primary spinal tumor surgeries be performed in experienced centers with multidisciplinary support teams and that prospective adverse event collection be promoted (strong recommendation/very low certainty of the evidence). Oncologic resection of primary tumors of the spine is associated with HRQOL that more closely approximates normative values with increasing duration of follow-up, but decreases with disease recurrence. We recommend primary spinal tumor surgery be performed with a curative intent whenever possible, even at the expense of greater initial morbidity to optimize long-term HRQOL (strong recommendation/very low certainty of the evidence). LEVEL OF EVIDENCE: N/A.
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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 | 0.014 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".