Adapting guidelines to support Intensive Care Unit survivorship : a pilot study
Bibliographic record
Abstract
Background: As ICU mortality decreases, it has been reported that over half of ICU survivors inadvertently experience post-intensive care syndrome (PICS). Comprehensive and consistent strategies to prevent, recognize and treat the physical, cognitive and psychological symptoms of PICS are in development in a large number of clinical settings nationally and internationally. There exists little research focusing on the implementation of PICS care pathways at the individual institutional level. Purpose: This was a pilot study and the first step in a quality improvement initiative. The purpose of this study was to examine whether the United Kingdom, National Institute for health and Clinical Excellence (NICE) 2009 guideline on rehabilitation after critical illness could be helpful to audit clinical practice in a Canadian hospital. Method: This was a pilot study that incorporated a retrospective chart audit. Data was collected from patient charts utilizing the NICE 2009 rehabilitation after critical illness audit support tool. Results: Not all of the proposed recommendations in the NICE 2009 guideline were applicable to the Canadian context. While the selected Canadian clinical setting in the study does not have an implemented PICS pathway, nine out of fifteen proposed audited recommendations were a part of usual care or based on health care professional expertise in the Canadian hospital. Conclusion: Collecting data on post-ICU recovery in the Canadian clinical setting utilizing the NICE 2009 rehabilitation after critical illness audit support tool is feasible. Clarification of the roles of multidisciplinary team members in the rehabilitation pathway and the auditing strategies to prevent, manage, and treat PICS throughout the ICU trajectory could potentially improve the usability and relevance of the NICE chart abstraction tool to the Canadian context. Implications: A PICS care pathway has the potential to optimize health outcomes of ICU survivors. To be effective, a multidisciplinary team approach is recommended in caring for the critically ill patient throughout the ICU trajectory. Nurse leaders, in collaboration with the multidisciplinary team, should review each proposed recommendation on rehabilitation after critical illness published by NICE to determine the applicability to local clinical settings in efforts to optimize ICU survivorship.
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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 | 0.022 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".