Identifying priorities and developing strategies for building capacity in amputation research in Canada
Bibliographic record
Abstract
BACKGROUND: Compared to other patient population groups, the field of amputation research in Canada lacks cohesion largely due to limited funding sources, lack of connection among research scientists, and loose ties among geographically dispersed healthcare centres, research institutes and advocacy groups. As a result, advances in clinical care are hampered and ultimately negatively influence outcomes of persons living with limb loss. OBJECTIVE: To stimulate a national strategy on advancing amputation research in Canada, a consensus-workshop was organized with an expert panel of stakeholders to identify key research priorities and potential strategies to build researcher and funding capacity in the field. METHODS: = 31 respondents) followed by an in-person consensus-workshop meeting that hosted 38 stakeholders (researchers, physiatrists, surgeons, prosthetists, occupational and physical therapists, community advocates, and people with limb loss). RESULTS: The top three identified research priorities were: (1) developing a national dataset; (2) obtaining health economic data to illustrate the burden of amputation to the healthcare system and to patients; and (3) improving strategies related to outcome measurement in patients with limb loss (e.g. identifying, validating, and/or developing outcome measures). Strategies for moving these priorities into action were also developed. CONCLUSIONS: The consensus-workshop provided an initial roadmap for limb loss research in Canada, and the event served as an important catalyst for stakeholders to initiate collaborations for moving identified priorities into action. Given the increasing number of people undergoing an amputation, there needs to be a stronger Canadian collaborative approach to generate the necessary research to enhance evidence-based clinical care and policy decision-making.IMPLICATIONS FOR REHABILITATIONLimb loss is a growing concern across North America, with lower-extremity amputations occurring due to complications arising from diabetes being a major cause.To advance knowledge about limb loss and to improve clinical care for this population, stronger connections are needed across the continuum of care (acute, rehabilitation, community) and across sectors (clinical, advocacy, industry and research).There are new surgical techniques, technologies, and rehabilitation approaches being explored to improve the health, mobility and community participation of people with limb loss, but further research evidence is needed to demonstrate efficacy and to better integrate them into standard clinical care.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".