The nursing workload assessed through the Nursing Activities Score as a predictor for the occurrence of ventilator-associated pneumonia in an adult intensive care unit
Bibliographic record
Abstract
Objective: Evaluate the relation of nursing workload, evaluated by the Nursing Activities Score (NAS), with the occurrence of Ventilator-associated Pneumonia (VAP) in an Intensive Care Unit (ICU) and the impact of VAP on hospitalization costs.Methods: Retrospective cohort study in Adult ICU of a high complexity Brazilian university hospital. The profile, outcomes, costs, and daily NAS from patients were collected. We also proposed some workload indicators based on NAS daily evaluation.Results: The study included 195 patients, 27.17% diagnosed with VAP. VAP was more prevalent in patients diagnosed with trauma on admission. The total costs of care were higher for VAP patients. In all multivariate models tested were predictive for VAP: the patient's intubation that occurs in days prior of the ICU admission day (higher risk if occurs in days prior the ICU admission day) and ventilation time prior ICU (higher risk if higher time). We found others predictors, but these were dependent on the model tested. Additional risk predictors were tracheostomy, propofol use, neuromuscular blocker use and the higher NAS from admission. The protective factors found were the percentage of adequacy of the assignment based in NAS that measure if the workload measured by the NAS was offered and the increment in NAS during the ventilation time.Conclusions: The offering of an adequate nursing work scale (adequate number of professionals for the care), as a function of the nursing workload measured by the NAS, could be effective in the reduction of VAP, hospital stay time and hospital costs.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".