Poster (Clinical/Best Practice Implementation) ID 1984775
Bibliographic record
Abstract
Background/Objectives Monitoring skin integrity is a critical issue for patients with spinal cord injury. Damage to the skin can go unnoticed due to sensory loss or diminution and can result in pressure injuries or wounds that can be difficult to heal. A team of inpatient clinicians and people with lived experience worked together to create a ‘SkIn-fo-Graphic’ that would be used to teach all new inpatients how to do a full body skin check. Methods A full picture of the body, contributed by Spinal Cord Injury Ontario (SCIO), was marked with the names of specific bony prominences and areas which should be viewed daily to ensure a skin check is complete. Staff at our Centre modified the graphic and created step-by-step instructions. Patients provided feedback on terminology and placement of words/ arrows for clarity. Further refinement was completed by SCIO and clinical staff to create the final tool and instructions. Results A graphic was developed iteratively by a community organization, physicians, allied health professionals and patients to provide a tool with instructions that can be used by both clinical staff (to teach daily skin check) and patients (as a reference for doing their own checks). A QR Code link was also created to directly link patients to more in-depth skin education on the community partner website. Conclusion Engaging all stakeholders in the development of a key tool for instruction of skin check in patients with spinal cord injury is important to ensure complete clarity and utility.
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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 | 0.007 | 0.004 |
| Insufficient payload (model declined to judge) | 0.806 | 0.550 |
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".