Prediction of Quality of Life and Survival After Surgery for Symptomatic Spinal Metastases
Bibliographic record
Abstract
BACKGROUND: Surgery for symptomatic spinal metastases aims to improve quality of life, pain, function, and stability. Complications in the postoperative period are not uncommon; therefore, it is important to select appropriate patients who are likely to benefit the greatest from surgery. Previous studies have focused on predicting survival rather than quality of life after surgery. OBJECTIVE: To determine preoperative patient characteristics that predict postoperative quality of life and survival in patients who undergo surgery for spinal metastases. METHODS: In a prospective cohort study of 922 patients with spinal metastases who underwent surgery, we performed preoperative and postoperative assessment of EuroQol EQ-5D quality of life, visual analog score for pain, Karnofsky physical functioning score, complication rates, and survival. RESULTS: The primary tumor type, number of spinal metastases, and presence of visceral metastases were independent predictors of survival. Predictors of quality of life after surgery included preoperative EQ-5D (P = .002), Frankel score (P < .001), and Karnofsky Performance Status (P < .001). CONCLUSION: Data from the largest prospective surgical series of patients with symptomatic spinal metastases revealed that tumor type, the number of spinal metastases, and the presence of visceral metastases are the most useful predictors of survival and that quality of life is best predicted by preoperative Karnofsky, Frankel, and EQ-5D scores. The Karnofsky score predicts quality of life and survival and is easy to determine at the bedside, unlike the EQ-5D index. Karnofsky score, tumor type, and spinal and visceral metastases should be considered the 4 most important prognostic variables that influence patient management.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".