Use of advanced topic modeling to generate domains for a preference-based index in osteoarthritis
Bibliographic record
Abstract
BACKGROUND: Health-related quality of life (HRQL) is an important endpoint when evaluating the effectiveness of interventions in people living with hip and knee osteoarthritis (OA). The aim of this study was to generate domains for a new OA-specific preference-based index of HRQL in people living with hip or knee OA. METHODS: The proposed HRQL index was based on a formative measurement model. The study included people aged 50 years and older, who reported being diagnosed with hip or knee OA. Participants reported the most important areas of their lives affected by OA. BERTopic method was used for topic modeling as part of Natural Language Processing. Hierarchical topic modeling was applied to merge similar topics together. RESULTS: A total of 102 people participated from across Canada. The participants had a mean age of 64.3 ± 7.6 years, and they reported having either knee (48.0%) or hip (16.7%) OA, or both (35.3%). Six major topics that affect the quality of life of people with OA emerged from the BERTopic analysis. Pain, going up and down stairs, walking, standing at home or work, sleep, and playing with grandchildren were the major concerns reported by people living with OA. CONCLUSION: This study used natural language processing to generate domains for a new OA-specific HRQL index that is based on the views of people living with hip or knee OA. Six domains important to people living with OA formed the construct of HRQL. The next steps will be to create items based on the topics generated from this analysis and elicit people's preferences for the different items.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".