Improving Equitable Access to Better Integrated Lower Limb Preservation Care and Supports
Bibliographic record
Abstract
Our region in Ontario, Canada, is experiencing high numbers of lower limb amputations. With earlier identification and improved access to ongoing preventative interventions, we know we may be able to avoid lower limb amputation and improve the quality of life and health outcomes for these patients. In early 2023, a Lower Limb Preservation Working Group was established to co-develop better integrated care pathways for populations at risk of lower limb amputations, specifically focusing on early intervention and preventative treatment. The working group focused on individuals with uncontrolled or poorly controlled diabetes who experience barriers to managing their disease, the high-risk population. Recent data from Ontario Health revealed that within our region there are 49,060 people living with a diagnosis of diabetes. Individuals who are not receiving primary care and individuals identifying as Indigenous or living within a First Nations community are demonstrating higher rates of risk factors associated with lower limb amputation. Through relationships with local patients/clients, care partners, providers, and health system administrators, a co-design process was used to collect individuals’ experiences, co-design solutions, and implement system improvement strategies. The working group engaged with populations of primary interest through detailed co-design methods including interviews, surveys, and focus groups that supported the group’s ability to assess the current state, map care journeys, and design, test, and implement interventions. An integrated care pathway was developed that focused on a co-designed central intake process that includes community partners providing wound care support and treatment specific to patient population based on need/preference in alignment with best practices for wound care; a more comprehensive diabetes support program; and acute care supports, when needed. Implementation has already demonstrated improved access to lower limb care for patients currently experiencing barriers to care including patients with no or precious attachment to primary care, patients deprived of stable housing, and patients that identify as First Nations, Inuit, Metis or Urban Indigenous. In addition, new technology adopted in the care pathway has allowed consistent measurement, standardized documentation, and tracking of wound progress while supporting communication of these data points across the continuum of care. Ongoing evaluation of the pathway aims to explore patient experience and quality of life, as well as system level outcomes. The Lower Limb Preservation group has highlighted system integration, and multidisciplinary teams as essential for preventative healthcare and enables more seamless transitions between modalities of care.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 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".