Calculating <i>ex-ante</i> Utilities From the Neck Disability Index Score: Quantifying the Value of Care For Cervical Spine Pathology
Bibliographic record
Abstract
STUDY DESIGN: General population utility valuation study. OBJECTIVE: To develop a technique for calculating utilities from the Neck Disability Index (NDI) score. METHODS: We recruited a sample of 1200 adults from a market research panel. Using an online discrete choice experiment (DCE), participants rated 10 choice sets based on NDI health states. A multi-attribute utility function was estimated using a mixed multinomial-logit regression model (MIXL). 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 AUC of .77 (95% CI: .76-.78). The regression model was used to develop a utility scoring rubric for the NDI. Regression results also revealed that participants did not regard all NDI items as equally important. The rank order of importance was (in decreasing order): pain intensity = work; personal care = headache; concentration = sleeping; driving; recreation; lifting; and lastly reading. CONCLUSIONS: This study provides a simple technique for converting the NDI score to utilities and quantify the relative importance of individual NDI items. The ability to evaluate quality-adjusted life-years using these utilities for cervical spine pain and disability could facilitate economic analysis and aid in allocation of healthcare resources.
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 | 0.014 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".