Identifying intensive care unit discharge planning tools: protocol for a scoping review
Bibliographic record
Abstract
BACKGROUND: Transitions of care between providers are vulnerable periods in healthcare delivery that expose patients to preventable errors and adverse events. Patient discharge from the intensive care unit (ICU) to a medical or surgical hospital ward is one of the most challenging and high risk transitions of care. Approximately 1 in 12 patients discharged will be readmitted to ICU or die before leaving the hospital. Many more patients are exposed to unnecessary healthcare, adverse events and/or are disappointed with the quality of their care. Our objective is to conduct a scoping review by systematically searching the literature to identify ICU discharge planning tools and their supporting evidence-base including barriers and facilitators to their use. METHODS AND ANALYSIS: Systematic searching of the published health literature will be conducted to identify the existing ICU discharge planning tools and supporting evidence. Literature (research and non-research) reporting on the tools used to facilitate decision making and/or communication at ICU discharge with patients of any age will be included. Outcomes will include adverse events and provider and patient/family-reported outcomes. Two investigators will independently review the abstracts (screen 1) to identify those meeting the inclusion criteria and then independently assess the full text articles (screen 2) to determine if they meet the inclusion criteria. Data collection will include information on citations and identified tools. A quality assessment will be performed on original research studies. A descriptive summary will be developed for each tool. ETHICS AND DISSEMINATION: Our scoping review will synthesise the literature for ICU discharge planning tools and identify the opportunities for knowledge to action and gaps in evidence where primary evidence is necessary. This will serve as the foundational element in a multistep research programme to standardise and improve the quality of care provided to patients during ICU discharge. Ethics approval is not required for this study.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.151 | 0.166 |
| Meta-epidemiology (narrow) | 0.006 | 0.007 |
| Meta-epidemiology (broad) | 0.014 | 0.015 |
| Bibliometrics | 0.017 | 0.018 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.006 | 0.009 |
| Research integrity | 0.011 | 0.010 |
| Insufficient payload (model declined to judge) | 0.091 | 0.019 |
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".