Bibliographic record
Abstract
OBJECTIVE: To systematically assess the literature to ascertain the pharmacokinetics, pharmacodynamics, and clinical efficacy and safety associated with administration of a vancomycin loading dose (LD). DATA SOURCES: MEDLINE (1948-December 31, 2014), EMBASE (1980-December 31, 2014), Cochrane Central Register of Controlled Trials, International Pharmaceutical Abstracts (1970-December 31, 2014), Google and Google Scholar, and International Clinical Trials Registry Platform were searched using the following terms: vancomycin, glycopeptides, loading dose, dose-response relationship. STUDY SELECTION AND DATA EXTRACTION: Pharmacokinetic, pharmacodynamic, and clinical efficacy studies using vancomycin LDs to achieve trough concentrations of 15 to 20 mg/L were included. Nonhuman, non-English, oral vancomycin, and dialysis patient studies were excluded. Abstracts were included. Study quality was ranked using US Preventative Services Task Force 1996 classification system. Data on study design, baseline characteristics, exclusion criteria, dosing, study outcomes, and conclusions were extracted. DATA SYNTHESIS: A total of 8 studies (5 manuscripts [2 level I, 3 level II-3] and 3 abstracts) were cited. Of 6 adult studies, 4 concluded that administration of vancomycin LDs resulted in significantly more patients achieving troughs of 15 to 20 mg/L. Studies in children found that LDs did not lead to rapid attainment of vancomycin levels ≥15 mg/L. No studies assessed clinical or microbiological outcomes. Limitations included heterogeneity and inconsistent timing of concentration measurements. CONCLUSIONS: High-quality data to guide the use of vancomycin LDs are lacking. LDs may more rapidly attain vancomycin troughs of 15 to 20 mg/L in adults, but information in pediatrics, obesity, and renal impairment is limited. Further studies are required to determine benefit of LDs on clinical and microbiological outcomes.
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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.023 | 0.004 |
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".