Surgical Adverse Events for Primary Tumors of the Spine and Their Impact on Outcomes: An Observational Study From the Primary Tumors Research and Outcomes Network
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Aggressive resection for primary tumors of the spine are associated with a high rate of adverse events (AEs), but the impact of AEs on patient-reported outcomes (PROs) remains unknown and is critical to the shared decision-making. Our primary objective was to assess the impact of surgical AEs on PROs using an international registry. Assessing the impact on clinical outcomes and identifying risk factors for AEs were our secondary objectives. METHODS: Patients who underwent surgery for a primary spinal tumor were selected through the Primary Tumor Research and Outcomes Network. Our primary outcome was the impact of AEs on PROs at 3 and 12 months after surgery (measured with Spinal Oncology Study Group Outcomes Questionnaire, Short-Form 36, and EuroQol 5 Dimension). We also assessed the impact on clinical outcomes (local control, surgical margins, readmission, reoperation, and mortality). We stratified our results according to severity of AEs, histology, and type of resection. RESULTS: 374 patients met inclusion criteria (219 males/155 females). The mean age of the cohort was 48.7 years. The most frequent histology was chordoma (37.3%) followed by chondrosarcoma (8.8%). Sixty-seven patients (17.9%) experienced at least 1 intraoperative AE and 117 patients (31.3%) had at least 1 postoperative AE within 3 months. Overall, 159 patients (42.5%) experienced AEs. The readmission rate was significantly higher in patients who experienced AEs (Any AE: 10.1% vs no AE: 1.9% within 3 months; P = <0.001). PROs were not significantly affected by AEs in most questionnaires. Local control, risk of reoperation, mortality, and achieving preplanned margins were similar between AE groups. CONCLUSION: The rate of surgical AEs is considerable in this population. Surgical AEs seem to be associated with a higher number of readmissions, but do not seem to result in significant differences in PROs or in a higher risk of reoperation, mortality, and failure to achieve preplanned margins.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".