ICU follow-up services and their impact on post-intensive care syndrome: a scoping review protocol
Bibliographic record
Abstract
INTRODUCTION: Post-intensive care syndrome (PICS) seriously affects the quality of life of intensive care unit (ICU) survivors, their ability to return to work and society and the quality of life of their families, increasing overall care costs and healthcare expenditures. ICU follow-up services have important potential to improve PICS. However, the best clinical practice model of ICU follow-up service has not been fully defined and its benefits for ICU survivors are not clear. This review will synthesise and map the current types of follow-up services for ICU survivors and summarise the impact of follow-up services on PICS. METHODS AND ANALYSIS: This scoping review will be conducted by applying the five-stage protocol proposed by Arksey and O'Malley in an updated version of the Joanna Briggs Institute. Eight academic databases including the Cochrane Library, MEDLINE, Web of Science, Embase, EBSCO Academic, CINAHL, PsycInfo and SinoMed (China Biology Medicine) will be systematically searched from inception to the present. Peer-reviewed literature and grey literature will be included. Qualitative, quantitative and mixed methods studies will be included. Studies published in English or Chinese will be included. There will be no time restriction. Two reviewers will screen and select the articles independently and if there is any disagreement, the two reviewers will discuss or invite a third reviewer to make decisions together. Descriptive analysis will be used to conduct an overview of the literature. The results will be presented in a descriptive format in response to the review questions accompanied by the necessary tables or charts. ETHICS AND DISSEMINATION: Ethical approval is not required for this scoping review because data could be obtained by reviewing published primary study results and do not involve human participants. Findings should be disseminated at scientific meetings and published in peer-reviewed journals.
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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 | 0.078 | 0.064 |
| Meta-epidemiology (narrow) | 0.005 | 0.006 |
| Meta-epidemiology (broad) | 0.016 | 0.013 |
| Bibliometrics | 0.021 | 0.016 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.007 | 0.007 |
| Research integrity | 0.009 | 0.005 |
| Insufficient payload (model declined to judge) | 0.062 | 0.008 |
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".