Appropriateness of basing vancomycin dosing on area under the concentration–time curve
Bibliographic record
Abstract
Appropriateness of basing vancomycin dosing on area under the concentration–time curve Bruce R Dalton, Pharm.D, Bruce R Dalton, Pharm.D Department of Pharmacy Services, Alberta Health Services, Calgary Zone, Calgary, Alberta, Canada bruce.dalton@ahs.ca Search for other works by this author on: Oxford Academic Google Scholar Deonne Dersch-Mills, Pharm.D., ACPR, Deonne Dersch-Mills, Pharm.D., ACPR Department of Pharmacy Services, Alberta Health Services, Calgary Zone, Calgary, Alberta, Canada Search for other works by this author on: Oxford Academic Google Scholar Ashten Langevin, Pharm.D, Ashten Langevin, Pharm.D Department of Pharmacy Services, Alberta Health Services, Calgary Zone, Calgary, Alberta, Canada Search for other works by this author on: Oxford Academic Google Scholar Deana Sabuda, B.Sc., B.S.P, Deana Sabuda, B.Sc., B.S.P Department of Pharmacy Services, Alberta Health Services, Calgary Zone, Calgary, Alberta, Canada Search for other works by this author on: Oxford Academic Google Scholar Elissa Rennert-May, M.D, Elissa Rennert-May, M.D Departments of Medicine and Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada Search for other works by this author on: Oxford Academic Google Scholar Thomas Griener, M.D., Ph.D Thomas Griener, M.D., Ph.D Department of Pathology and Laboratory Medicine, University of Calgary, Calgary, Alberta, Canada Search for other works by this author on: Oxford Academic Google Scholar American Journal of Health-System Pharmacy, Volume 76, Issue 21, 1 November 2019, Pages 1718–1721, https://doi.org/10.1093/ajhp/zxz184 Published: 15 October 2019
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".