The provision of ICU outreach and its impact on ICU survivors, families and healthcare professionals
Bibliographic record
Abstract
Patients who need critical care are among the sickest in a hospital and require a high level of clinical and technical expertise (Department of Health, 2005). There has been a considerable amount of evidence on the effectiveness of the care this patient group receives and much of this resulted in the assertion that critical care should be regarded as a description of a patient’s care needs rather than a place of care (DH, 2000), and a recommendation that Critical and Intensive care outreach services be developed. (Hainsworth, 2006). Research has shown that providing early intervention to patients with deteriorating conditions by critical care health care providers outside the walls of the ICU decreases the incidence death following cardiac arrest, length of hospital stay related to cardiac arrest, post-operative adverse outcomes, post-operative mortality rate, and length of hospital stay (Ball, Kirkby, & William, 2003; Bellomo et al., 2003 DeVita et al., 2004). It was shown that 30 percent the number of documented cardiac arrests after implementation of a critical care team that works outside of ICU (Canadian Healthcare Technology, 2007) decreased. In Malaysia however, introduction of ICU outreach nurse service is rather unclear. From the year of 2007 to 2011, there were increased about 81% of admission and readmission occur within 48 to 72 hours and it was commonly used as an indicator care for patient management that reflect premature ICU discharge or substandard ward by general ward staffs (Tong et.al, 2011). Thus, is the establishment of ICU Outreach services in Malaysia a need?.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".