International phase 1 study protocol to develop a health state classification system for a preference-based measure for women with breast cancer: the BREAST-Q Utility module
Bibliographic record
Abstract
INTRODUCTION: Concerns unique to women with breast cancer can include impact of cancer on body image, sexual well-being and changes in breast appearance and sensation. These important issues are not captured by the existing generic preference-based measures (PBMs) and no breast cancer-specific PBM currently exists. This Phase 1 protocol describes a mixed-methods study to develop and validate the descriptive health state classification system for a breast cancer-specific PBM, called the BREAST-Q Utility module. METHODS AND ANALYSIS: A heterogeneous sample of women aged 18 years and older diagnosed with breast cancer who are undergoing or have had treatment for breast cancer will be invited to participate in qualitative interviews. Participants will be asked to describe impact of their diagnosis and treatment(s) on their health-related quality of life (HRQOL). Interviews will be audio recorded, transcribed verbatim and coded using a line-by-line approach. At the end of each interview, based on each participant's cancer treatment history, patients will complete the mastectomy, breast-conserving therapy or reconstruction module of BREAST-Q, with modified 5-point Likert scale to measure importance of the BREAST-Q concepts. Both sources of data will be analysed to identify the most important HRQOL concerns.A conceptual framework and item pool will be developed from the qualitative dataset. Preliminary version of the BREAST-Q Utility module will be created and refined at an in-person meeting of multidisciplinary experts. Content validity of the Utility module will be examined (cognitive debriefing, expert feedback). Psychometric properties of Utility module will be evaluated in a large sample of women with breast cancer. ETHICS AND DISSEMINATION: The study has been approved by Hamilton Integrated Research Ethics Board, Canada. Results of this study will be presented at international conferences and published in peer-reviewed journals.
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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 | 0.054 | 0.047 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.108 | 0.022 |
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".