Poster (Clinical/Best Practice Implementation) ID 1985182
Bibliographic record
Abstract
Background Persons with spinal cord injury (SCI) are at increased risk of developing pressure injuries throughout their lifetime. This significant yet preventable secondary complication can have a negative impact on one’s health and well-being. A key aspect of prevention is performing regular daily skin checks; however, a gap in knowledge is apparent among clinicians and patients on how exactly to perform them. Objective To develop a universal and widely available skin check video resource that supports clinicians and patients. Methods Clinicians across the SCI-IEQCC Network from Parkwood Institute, Hamilton Regional Reha-bilitation Centre, Ottawa Hospital Rehabilitation Center, Lyndhurst, Providence Care, and in partnership with SCIO, Cortree and persons with lived experiences, all contributed in an iterative manner to the development of a skin check video resource. Feedback from all relevant stakeholders was gathered after each round of edits to ensure the content would meet the educational needs of persons with lived experience and rehabilitation staff. Results This collaboration allowed for the development of an open-source skin check video resource for both clinicians and persons with lived experience. This video is in process of being integrated within patient skin check education of the various rehabilitation sites across Ontario. The video identifies key factors to consider when completing skin checks and demonstrates the technique on how to complete skin checks independently and with assistance. Conclusion While a successful skin check video resource was created, next steps will look to its sustainable implementation and dissemination at a local and provincial level.
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.726 | 0.394 |
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".