Monitoring Parenteral Nutrition in Hospitalized Patients
Bibliographic record
Abstract
BACKGROUND: Monitoring hospitalized patients receiving parenteral nutrition requires regular bloodwork. However, blood specimens, if not drawn appropriately, may be contaminated by parenteral nutrition, leading to spurious results and unnecessary medical interventions. The objective was to determine, in a large academic center, the frequency of spurious bloodwork, unnecessary medical interventions, and contributing factors. METHODS: This was a 1-year prospective cohort study monitoring hospitalized patients receiving parenteral nutrition and their bloodwork. Sudden unexplained changes in serum levels of glucose, potassium, and sodium were identified. Subsequent medical interventions were tracked. Factors affecting blood collection, such as technique, shifts, nursing units, nursing, and patient demography, were assessed and compared with those of a control group. RESULTS: Out of 201 patients, 34 had 63 incidents of spurious bloodwork. This led to 23 medical interventions. The most frequent problem was the failure to clamp the parenteral nutrition infusion prior to blood collection or too short a time between clamping and drawing. There was an increased occurrence of spurious bloodwork drawn by nurses with < 10 years of experience due to failure in following blood collection policy. Cost of spurious bloodwork and subsequent interventions for 63 incidents was approximately $3480 (CAD) per year. This excluded physician time. CONCLUSIONS: Spurious bloodwork was due to parenteral nutrition contamination by incorrect blood draw techniques. This led to a policy amendment to incorporate a "wait time" between stopping the parenteral nutrition infusion and drawing blood and to an institution-wide nursing reeducation.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".