The Psychometric Properties of a Self-Administered, Open-Source Module for Valuing Metastatic Epidural Spinal Cord Compression Utilities
Bibliographic record
Abstract
INTRODUCTION: Web surveys are often used for utility valuation. Typically, custom utility valuation tools that have not undergone psychometric evaluation are used. OBJECTIVES: This study aimed to determine the psychometric properties of a metastatic epidural spinal cord compression (MESCC) module run on a customizable open-source, internet-based, self-directed utility valuation platform (Self-directed Online Assessment of Preferences [SOAP]). METHODS: Individuals accompanying patients to the emergency department waiting room in Ottawa, Canada, were recruited. Participants made SOAP MESCC health state valuations in the waiting room and 48 h later at home. Validity, agreement reliability, and responsiveness were measured by logical consistency of responses, smallest detectable change, the interclass correlation coefficient, and Guyatt's responsiveness index, respectively. RESULTS: Of 285 participants who completed utility valuations, only 113 (39.6%) completed the re-test. Of these 113 participants, 92 (81.4%) provided valid responses on the first test and 75 (66.4%) provided valid responses on the test and re-test. Agreement for all groups of health states was adequate, since their smallest detectable change was less than the minimal clinically important difference. The mean interclass correlation coefficients for all health states were > 0.8, indicating at least substantial reliability. Guyatt's responsiveness indices all exceeded 0.80, indicating a high level of responsiveness. CONCLUSIONS: To our knowledge, this is the first validated open-source, web-based, self-directed utility valuation module. We have demonstrated the SOAP MESCC module is valid, reproducible, and responsive for obtaining ex ante utilities. Considering the successful psychometric validation of the SOAP MESCC module, other investigators can consider developing modules for other diseases where direct utility valuation is needed.
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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 | 0.010 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| 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".