Predictors of Clinical Deterioration of Hospitalized Adult Medical Patients
Bibliographic record
Abstract
Clinical deterioration of hospitalized medical patients negatively affects patient outcomes and hospital capacity. Failure to recognize and respond promptly to an individual's worsening health status can lead to complications with far-reaching impacts on the patient and family. The ability to identify patient cues that can predict clinical deterioration is an essential role for frontline health-care providers to avert an avoidable health crisis. This protocol is designed to describe an integrative literature review plan that aims to identify, analyze, and synthesize the predictors and associated factors underlying the clinical deterioration of hospitalized medical ward patients. This planned review will follow the methodology of Whittemore and Knafl (2005), which comprises five stages: problem identification, literature search, data evaluation, data analysis, and presentation. CINAHL Plus, Embase, and PubMed databases will be used in the literature search. Primary research studies focusing on the predictors or the associated factors of clinical deterioration among medical ward patients will be eligible for the review. The quality of selected articles will be critically appraised using the Joanna Briggs Institute tools. The process of findings synthesis will be conducted according to Miles and Huberman (1994), which consists of data reduction, data display, data comparison, conclusion drawing, and verification. The findings will be presented as major themes that are supported by the appropriate primary studies.
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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 | 0.004 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".