Calculating Utilities From the Spine Oncology Study Group Outcomes Questionnaire
Bibliographic record
Abstract
STUDY DESIGN: General population utility valuation study. OBJECTIVE: The aim of this study was to develop a technique for calculating utilities from the Spine Oncology Study Group Outcomes Questionnaire v2.0 (SOSGOQ2.0). SUMMARY OF BACKGROUND DATA: The ability to calculate quality-adjusted life-years (QALYs) for metastatic spine disease would enhance treatment decision-making and facilitate economic analysis. QALYs are calculated using utilities. METHODS: Using a hybrid concept-retention and factorial analysis shortening approach, we first shortened the SOSGOQ2.0 to eight items (SOSGOQ-8D). This was done to lessen the cognitive burden of the utility valuation exercise. A general population sample of 2730 adults was then asked to evaluate 12 choice sets based on SOSGOQ-8D health states in a Discrete Choice Experiment. A utility scoring rubric was then developed using a mixed multinomial-logit regression model. RESULTS: We were able to reduce the SOSGOQ2.0 to an SOSGOQ-8D with a mean error of 0.003 and mean absolute error of 3.078 compared to the full questionnaire. The regression model demonstrated good predictive performance and was used to develop a utility scoring rubric. Regression results revealed that participants did not regard all SOSGOQ-8D items as equally important. CONCLUSION: We provide a simple technique for converting the SOSGOQ2.0 to utilities. The ability to evaluate QALYs in metastatic spine disease will facilitate economic analysis and patient counseling. We also quantify the importance of individual SOSGOQ-8D items. Clinicians should heed these findings and offer treatments that maximize function in the most important items.Level of Evidence: 3.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.018 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".