Calculating Ex-ante Utilities From the Modified Japanese Orthopedic Association Score
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 modified Japanese Orthopedic Association (mJOA) Score. Summary of Background Data. The ability to calculate quality-adjusted life-years (QALYs) for degenerative cervical myelopathy (DCM) would enhance treatment decision making and facilitate economic analysis. QALYs are calculated using utilities. Methods. We recruited a sample of 760 adults from a market research panel. Using an online discrete choice experiment, participants rated eight choice sets based on mJOA health states. A multiattribute utility function was estimated using a mixed multinomial-logit regression model. The sample was partitioned into a training set used for model fitting and validation set used for model evaluation. Results. The regression model demonstrated good predictive performance on the validation set with an area under the curve of 0.81 (95% confidence interval: 0.80–0.82)). The regression model was used to develop a utility scoring rubric for the mJOA. Regression results revealed that participants did not regard all mJOA domains as equally important. The rank order of importance was (in decreasing order): lower extremity motor function, upper extremity motor function, sphincter dysfunction, upper extremity sensation. Conclusion. This study provides a simple technique for converting the mJOA score to utilities and quantify the importance of mJOA domains. The ability to evaluate QALYs for DCM will facilitate economic analysis and patient counseling. Clinicians should heed these findings and offer treatments that maximize function in the attributes viewed most important by patients. Level of Evidence: 3
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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 | 0.013 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".