Systematic review of physical and chemical compatibility of commonly used medications administered by continuous infusion in intensive care units
Bibliographic record
Abstract
OBJECTIVE: To quantify the physical and chemical stability data published for commonly used continuously infused medications in the intensive care unit and to evaluate the quality of the studies providing these data. DATA SOURCES AND STUDY SELECTION: We conducted a systematic electronic literature search of MEDLINE, EMBASE, and International Pharmaceutical Abstracts as well as the references of electronic drug compatibility textbooks for all English and French language research publications evaluating the physical compatibility or chemical stability of the 820 possible two-drug combinations of 41 commonly used drugs in an adult intensive care unit. DATA EXTRACTION AND SYNTHESIS: A total of 93 studies comprised of 86 (92%) studies evaluating physical compatibility and 35 (38%) studies evaluating chemical compatibility of at least one drug combination of interest were included. Physical and/or chemical compatibility data exist for only 441 of the possible 820 two-drug combinations (54%), whereas chemical compatibility data exist for only 75 (9%) of the possible combinations. Of the 441 combinations for which compatibility data are available, 67 (15%) represent incompatible combinations and 39 (9%) had conflicting data in which both compatible and incompatible data were identified. CONCLUSIONS: Physical compatibility studies that provide the basis for y-site compatibility are lacking for commonly used medications in intensive care unit patients and may contribute to unsafe medication practices. Furthermore, the heterogeneity in the methodology of these studies likely contributes to the common finding of conflicting data for specific combinations of drugs. Future studies should apply similar methodologic and reporting principles to be able to reproduce and compare outcomes both clinically and in the laboratory.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.016 | 0.096 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.007 |
| Bibliometrics | 0.018 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".