Predicting the ICECAP-O Capability Index from the WOMAC Osteoarthritis Index
Bibliographic record
Abstract
BACKGROUND: There is a growing interest in the application of the capability approach in health economic analysis. The aim of the research reported here is to assess the feasibility of mapping from a condition-specific questionnaire (Western Ontario and McMaster Universities [WOMAC] Osteoarthritis Index) to a capability well-being questionnaire (ICEpop CAPability measure for Older people [ICECAP-O]). METHODS: . One hundred five osteoarthritis patients requiring joint replacement completed the 5 attributes on the ICECAP-O (attachment, security, role, enjoyment, and control) and the 3 WOMAC categories (pain, stiffness, and physical function). The prediction data set consisted of baseline scores, whereas follow-up data were used to validate the predictions. The mapping algorithms used ordinary least squares and multinomial logistic regression models to predict the relationship between WOMAC scores, categories, or items and ICECAP-O scores or the 5 ICECAP-O attributes. RESULTS: . ICECAP-O scores predicted from WOMAC category scores produced the lowest mapping error statistics (mean absolute error = 0.0832; mean squared error = 0.0142) as well as highest goodness of fit (R(2) = 0.3976). Prediction of ICECAP-O attributes from WOMAC category scores was possible for the majority of capability dimensions. The "control" attribute and physical function WOMAC category exhibited the strongest relationship (R(2) = 0.2143). The "attachment" attribute proved difficult to predict from any WOMAC category, which is in line with intuition given this attribute captures psychological well-being rather than pain, stiffness, or physical function. CONCLUSION: . This is the first study to investigate the predictive ability of a condition-specific measure of health onto capability. The results presented here suggest it is feasible to map from condition-specific measures to an overall capability index, although WOMAC cannot predict individual capability in its entirety. Although the results here are encouraging for those interested in using ICECAP-O, given the small validation sample size applied, further research will be required to verify these findings.
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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 | 0.023 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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; both teacher heads agree on what is shown here.
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".