Health solutions to improve post-intensive care outcomes: a realist review protocol
Bibliographic record
Abstract
BACKGROUND: While 80% of critically ill patients treated in an intensive care unit (ICU) will survive, survivors often suffer a constellation of new or worsening physical, cognitive, and psychiatric complications, termed post-intensive care syndrome. Emerging evidence paints a challenging picture of complex, long-term complications that are often untreated and culminate in substantial dependence on acute care services. Clinicians and decision-makers in the Fraser Health Authority of British Columbia are working to develop evidence-based community healthcare solutions that will be successful in the context of existing healthcare services. The objective of the proposed review is to provide the theoretical scaffolding to transform the care of survivors of critical illness by a synthesis of relevant clinical and healthcare service programs. METHODS: Realist review will be used to develop and refine a theoretical understanding of why, how, for whom, and in what circumstances post-ICU program impact ICU survivors' outcomes. This review will follow the recommended five steps of realist review which include (1) clarifying the scope of the review and articulating a preliminary program theory, (2) searching for evidence, (3) appraising primary studies and extracting data, (4) synthesizing evidence and sharing conclusions, and (5) disseminating and implementing recommendations. DISCUSSION: This realist review will provide a program theory, encompassing the contexts, mechanisms, and outcomes, to explain how clinical and health service interventions to improve ICU survivor outcomes operate in different contexts for different survivors, and with what effect. This review will be an evidentiary pillar for health service development and implementation by our knowledge user team members as well as advance scholarly knowledge relevant nationally and internationally. SYSTEMATIC REVIEW REGISTRATION: PROSPERO CRD42018087795.
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 | 0.101 | 0.130 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.010 | 0.012 |
| Bibliometrics | 0.014 | 0.013 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.067 | 0.012 |
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".