ABSTRACT 105
Bibliographic record
Abstract
Background and aims: The ideal intensivist: patient ratio has not been established. As a pediatric intensive care unit (PICU) increases in size, many centers move to a two teams model, to improve flow in rounds and provide better care. Aims: We aimed to determine if dividing our PICU in two care teams has improved acute care outcomes reflecting better care of critically ill children. Methods: Retrospective cohort study comparing two different time periods at the Stollery Children’s Hospital. PICU was divided in a cardiac (CV) team and a medical/surgical (MS) team in 2009. The ‘One-team ‘ era (2007 - 2008) was compared to the ‘Two-teams’ era (2010 - 2011). The following variables were considered acute outcomes: days on Mechanical Ventilation (MV), PICU length of stay (LOS), central line associated infections (CLAI), ventilator associated pneumonias (VAP), PICU mortality and hospital mortality. Multivariable regression was used for the analysis. Study approved by institutional health ethics research board. Results: There were 1533 admissions in the ‘One-team ‘ era and 1817 in the ‘Two-teams’ era. During the ‘One-team’ era, each intensivist was responsible for an average of 14.7 patients/week, while in the ‘Two-teams’ era it was for 8.4 and 9.3 patients/week in the CV and MS teams respectively. After adjusting for other variables, multivariable regression analysis found that the ‘Two-teams’ era was significantly associated with fewer days on MV and shorter PICU LOS; no change in mortality, CLAI or VAP rates was detected. Conclusions: Two-teams with a lower patient:intensivist ratio was associated with fewer days on MV and shorter PICU LOS.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.626 | 0.492 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".