Workshop (Knowledge Generation) ID 2001657
Bibliographic record
Abstract
Background Multi-morbidity is common in persons with spinal cord injury (SCI). Network Analysis is a tool used to visualize and estimate complex relationships among variables. Three network models: Gaussian Graphical Model, Ising model, and Mixed Graphical Model were applied to the 2011-2012 Canadian SCI Community Survey dataset, which included individuals with traumatic and non-traumatic SCI. Data utilized included demographic and injury data as we well as 30 secondary health conditions (comorbidities and secondary complication) that are included in the Multi-Morbidity Index (MMI-30). Five health outcomes were included: healthcare utilization (HCU), health status (i.e. Short Form-12 physical and mental component summary (SF-12 PCS & MCS) score), life satisfaction, and quality of life. Using Network Analysis, we reduced the number of items in the Multi-Morbidity Index (MMI-30) by 5 items (MMI-25) and the psychometric properties were comparable. This interactive workshop will include presentations from a clinician, researcher and person with lived experience (PLEX). Objective The goals of this workshop are to: Review Machine Learning methodology (with an emphasis on Network Analysis) in SCI research. Demonstrate how Network Analysis was used to investigate the relationship between secondary health conditions and health outcomes to create a modified version of the Multi-Morbidity Index. Discuss future opportunities for using Network Analysis and other Machine Learning methodologies in SCI research and provide examples of how these knowledge products can be used to inform SCI care. Conclusion This workshop will demonstrate the benefit of using Network Analysis, a type of Machine Learning, in SCI research. Specifically, the example of how Network Analysis identified key associations among 30 secondary health conditions and five health outcomes which resulted in the MMI-25 will be discussed as well as future opportunities for using Network Analysis and other Machine Learning methodologies in SCI research.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.803 | 0.586 |
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; the direct Gemma label and the distilled Codex classifier 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".