Reducing endocrine metabolic disease risk in adults with chronic spinal cord injury: strategic activities conducted by the Ontario-Quebec RIISC team
Bibliographic record
Abstract
PURPOSE: ommunity (RIISC) team aimed to develop and evaluate innovative rehabilitation interventions to identify endocrine metabolic disease (EMD) risk, intending to reduce the frequency and severity of EMD related morbidity and mortality among adults living with chronic spinal cord injury or disease (SCI/D). MATERIALS AND METHODS: An interprovincial team from Ontario and Quebec reviewed available EMD literature and evidence syntheses and completed an inventory of health services, policies and practices in SCI/D care. The review outcomes were combined with expert opinion to create an EMD risk model to inform health service transformation. RESULTS: EMD risk and mortality are highly prevalent among adults with chronic SCI/D. In stark contrast, few rehabilitation interventions target EMD outcomes. The modelled solution proposes: 1) abandoning single-disease paradigms and examining a holistic perspective of the individual's EMD risk, and 2) developing and disseminating practice-based research approaches in outpatient community settings. CONCLUSIONS: RIISC model adoption could accelerate EMD care optimization, and ultimately inform the design of large-scale longitudinal pragmatic trials likely to improve health outcomes. Linking the RIISC team activities to economic evaluations and policy deliverables will strengthen the relevance and impact among policymakers, health care providers and patients.
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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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".