Poster (Clinical/Best Practice Implementation) ID 2004606
Bibliographic record
Abstract
Background/Objectives The Centre for Family Medicine Primary Care Mobility Clinic has been active in developing collaborations at various institutions to further the overall research and clinical objectives of serving individuals with spinal cord injuries (SCI). From these initiatives, The Circulus SCI Primary Care Network (Circulus Network) was established to spark intentional collaborative research, education, training and service in primary care for individuals with SCI. Objectives The Circulus Network aims to bridge the gaps in primary care for individuals with SCI through research, education, increasing primary care resources, networking with various stakeholders and general advocacy. Methods/Overview The Circulus Network is comprised of a Steering Committee and working groups that are representative of key stakeholders. These groups meet on a rotating monthly basis and are supported by a group process consultant. The Circulus Network informs best practice guidelines and improves knowledge translation by hosting quarterly interactive webinars on topics related to SCI and primary care as well as bi-annual Summits. Results Since 2021, The Circulus Network has hosted 9 webinars on SCI-related topics with good attendance by network participants. It also hosted a Summit in 2021 in which participants discussed priority planning topics and leveraged expertise to develop action steps. The Steering Committee is planning another Summit for February 2024. Conclusions Using our extensive knowledge of primary care for individuals with SCI, we will continue to strengthen and develop the formalized network of patients, care partners, clinicians, researchers, and educators to support primary care of individuals with spinal cord injuries.
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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 | 0.012 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.008 | 0.005 |
| Insufficient payload (model declined to judge) | 0.719 | 0.349 |
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".