Developing a patient-centered outcome measure for complementary and alternative medicine therapies I: defining content and format
Bibliographic record
Abstract
BACKGROUND: Patients receiving complementary and alternative medicine (CAM) therapies often report shifts in well-being that go beyond resolution of the original presenting symptoms. We undertook a research program to develop and evaluate a patient-centered outcome measure to assess the multidimensional impacts of CAM therapies, utilizing a novel mixed methods approach that relied upon techniques from the fields of anthropology and psychometrics. This tool would have broad applicability, both for CAM practitioners to measure shifts in patients' states following treatments, and conventional clinical trial researchers needing validated outcome measures. The US Food and Drug Administration has highlighted the importance of valid and reliable measurement of patient-reported outcomes in the evaluation of conventional medical products. Here we describe Phase I of our research program, the iterative process of content identification, item development and refinement, and response format selection. Cognitive interviews and psychometric evaluation are reported separately. METHODS: From a database of patient interviews (n = 177) from six diverse CAM studies, 150 interviews were identified for secondary analysis in which individuals spontaneously discussed unexpected changes associated with CAM. Using ATLAS.ti, we identified common themes and language to inform questionnaire item content and wording. Respondents' language was often richly textured, but item development required a stripping down of language to extract essential meaning and minimize potential comprehension barriers across populations. Through an evocative card sort interview process, we identified those items most widely applicable and covering standard psychometric domains. We developed, pilot-tested, and refined the format, yielding a questionnaire for cognitive interviews and psychometric evaluation. RESULTS: The resulting questionnaire contained 18 items, in visual analog scale format, in which each line was anchored by the positive and negative extremes relevant to the experiential domain. Because of frequent informant allusions to response set shifts from before to after CAM therapies, we chose a retrospective pretest format. Items cover physical, emotional, cognitive, social, spiritual, and whole person domains. CONCLUSIONS: This paper reports the success of a novel approach to the development of outcome instruments, in which items are extracted from patients' words instead of being distilled from pre-existing theory. The resulting instrument, focused on measuring shifts in patients' perceptions of health and well-being along pre-specified axes, is undergoing continued testing, and is available for use by cooperating investigators.
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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 | 0.053 | 0.081 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".