Nursing resources and patient outcomes in intensive care units
Bibliographic record
Abstract
BACKGROUND: As nursing resources is directly related to patient outcomes in the intensive care unit setting, identifying factors related to nursing resources at various levels could contribute to improving those outcomes. This study aims to determine the association of nursing resources with outcomes of intensive care unit patients. METHOD: This study adheres to the Preferred Reporting Items for Systematic Reviews and Meta-analysis for Protocols. Chinese electronic Database (Chinese Biomedical Literature Database, Wanfang, and China National Knowledge Infrastructure) and international electronic databases (PubMed, Embase, Cochrane Library, and Web of Science) will be searched for all relevant published articles, with no restrictions on the year of publication or language. Study selection, data collection and assessment of study bias will be conducted independently by a pair of independent reviewers. The Newcastle-Ottawa Scale tool will be used for the risk of bias assessment. The Grading of Recommendations Assessment Development and Evaluation system will be used to assess the quality of evidence. The statistical analysis of this meta-analysis will be calculated by Review manager version 5.3. RESULTS: The results of this study will be published in a peer-reviewed journal. CONCLUSION: The findings of this systematic review will provide a high-quality synthesis of latest evidence and provide a basis for assessing the association of nursing resources on patients' outcomes in intensive care units. TRIAL REGISTRATION NUMBER: 10.17605/OSF.IO/9FNEX.
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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 | 0.015 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".