487 Improving Quality for Burn Patients in a General Intensive Care Unit
Bibliographic record
Abstract
The objective of this project was to identify opportunities to improve care for burn patients in a general systems intensive care unit. The 32 bed mixed medical surgical trauma unit admits more than 1450 patients per year and is the referral centre for the region. However, this population represents a small proportion, roughly 1 per cent, of all yearly admissions leading to ongoing challenges with consistency and processes. A burn working group was formed in November 2016 to set patient care priorities, engage bedside care providers and enhance collaboration. Meetings are attended by staff nurses, intensive care and burn physicians, nurse educators, allied health staff and administration. All ideas for improvement are shared in a round table and then prioritized collaboratively. The group meets monthly to review progress and priorities. Smaller teams work on specific items as necessary. Accomplishments include a quantitative review of burn resuscitation indicators, creating a burn dressing cart to improve efficiency during burn procedures, engaging new stakeholders (e.g. physiotherapy, emergency nurse educators), collaborating with microbiology/infection control to clarify misinformation, policy change to promote early mobilization, organizational support for additional nursing resources, a literature review about pain management during burn procedures, revising intensive care nurse education and standardizing wound care practice. Qualitative feedback from team members and intensive care staff is overwhelmingly positive.There have been noticeable improvements in consistency in process as well as improved collaboration between the intensive care and the burn teams. The group is continuing to measure key indicators and looking for new opportunities for improvement. Setting priorities collaboratively allowed this group to address a diverse set of patient care issues. Actively seeking the perspectives of many stakeholders including nurses, physicians, and allied health professionals from both the intensive care and burns specialties has broken down silos and revealed opportunities for improvement that would have otherwise remained unaddressed. Encouraging individuals to work on the issues in which they are the most invested has accelerated positive change and helped maintain momentum. This is a low-cost intervention that has had a significant impact on staff and patients at our site.
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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 | 0.008 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".