Correction: Survivorship of Patients After Long Intensive Care Stay With Exploration and Experience in a New Zealand Cohort (SPLIT ENZ): Protocol for a Mixed Methods Study (Preprint)
Bibliographic record
Abstract
UNSTRUCTURED Post Intensive Care Syndrome (PICS) was defined by the Society of Critical Care Medicine in 2012 with subsequent international research highlighting poor long-term outcomes; reduced quality of life; and impairments, for survivors of critical illness. To date, there has been no published research on the long-term outcomes of survivors of critical illness in New Zealand. The aim of this study is to explore long-term outcomes after critical illness in New Zealand. The primary objectives are to describe and quantify symptoms and disability, explore possible risk factors, and to identify unmet needs in survivors of critical illness. This will be a mixed methods study with 2 components. First, a prospective cohort study of approximately 100 participants with critical illness will be followed up at 1, 6, and 12 months after hospital discharge. The primary outcome will be disability assessed using the World Health Organization Disability Assessment Scale 2.0. Secondary outcomes will focus on mental health using the Hospital Anxiety and Depression Scale and the Impact of Events Scale-revised, cognitive function using the Montreal Cognitive Assessment (Montreal Cognitive Assessment–BLIND), and health-related quality of life using the European Quality of Life-Five Dimension-Five Level. The second element of the study will use qualitative grounded theory methods to explore participants experiences of recovery and highlight unmet needs. This study was approved by the New Zealand Northern A Health and Disability Ethics Committee on August 16, 2021 (21/NTA/107), and has been registered with the Australian New Zealand Clinical Trials Registry on October 5, 2021. SPLIT ENZ is due to start recruitment in early 2022, aiming to enroll 125 patients over 2 years. Data collection is estimated to be completed by 2024-2025 and will be published once all data are available for reporting. Although international research has identified the prevalence of PICS and the extent of disability in survivors of critical illness, there is no published research in New Zealand. Research in this field is particularly pressing in the context of COVID-19, an illness that may include PICS in its sequelae. Australian New Zealand Clinical Trials Registry ACTRN1262100133588; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=382566&showOriginal=true&isReview=true
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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 | 0.020 | 0.163 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.168 | 0.017 |
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".