Effectiveness of predictive scoring systems in predicting mortality in relation to baseline kidney function in adult intensive care unit patients: a systematic review protocol
Bibliographic record
Abstract
INTRODUCTION: Predictive scoring systems support clinicians in decision-making by estimating the prognosis of patients in intensive care units (ICUs). However, there is limited evidence on the accuracy of these systems in predicting mortality and organ dysfunction in special populations. The aim of this review is to assess the performance of predictive scoring systems in forecasting mortality in adult ICU patients in relation to baseline kidney function. It is anticipated that the assessment of predictive scoring systems' performance and patient outcomes in this review may reveal information that will contribute to improve the quality of care and outcomes for special or under-represented ICU patient populations. It might also inform future research and contribute to the development of novel risk prediction models to address identified gaps or unanswered questions. METHODS AND ANALYSIS: This review will include only observational studies, as these allow us to assess the real-world performance of predictive scoring systems in ICU settings by examining the original validation studies. By excluding randomised trials, paediatric studies, case reports and machine learning-derived models, this review focuses on the direct practical use of the scoring systems in adult ICU patients. A comprehensive search of MEDLINE, Embase and Scopus was conducted from database inception to 10 October 2024. The data will be extracted on study characteristics, patient outcomes and performance metrics. ETHICS AND DISSEMINATION: This review will analyse data from previously published studies; no ethical approval is required. All data that will be included in the analysis will be publicly available and will be included in the final manuscript. Results will be disseminated through publication in a peer-reviewed journal and will also be presented at seminars and conferences. PROSPERO REGISTRATION NUMBER: CRD42024611547.
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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 | 0.034 | 0.051 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.019 | 0.014 |
| Bibliometrics | 0.015 | 0.011 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.031 | 0.003 |
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".