Use of a personal digital assistant in a pharmacy-directed warfarin dosing program
Bibliographic record
Abstract
Journal Article Use of a personal digital assistant in a pharmacy-directed warfarin dosing program Get access Fran Paradiso-Hardy, B.Sc.Pharm., M.Sc., FCSHP, Fran Paradiso-Hardy, B.Sc.Pharm., M.Sc., FCSHP Clinical Coordinator, Cardiovascular Diseases Pharmacy Department Sunnybrook and Women’s College Health Sciences Centre 2075 Bayview Avenue Toronto, Ontario M4N 3M5, Canada Search for other works by this author on: Oxford Academic Google Scholar Ada Seto, B.Sc.Pharm., Ada Seto, B.Sc.Pharm. Pharmacy Practice Resident Pharmacy Department Sunnybrook and Women’s College Health Sciences Centre 2075 Bayview Avenue Toronto, Ontario M4N 3M5, Canada Search for other works by this author on: Oxford Academic Google Scholar Stephanie Ong, B.Sc.Pharm., Stephanie Ong, B.Sc.Pharm. Clinical Cardiovascular Pharmacist Pharmacy Department Sunnybrook and Women’s College Health Sciences Centre 2075 Bayview Avenue Toronto, Ontario M4N 3M5, Canada Search for other works by this author on: Oxford Academic Google Scholar Claudia Bucci, B.Sc.Pharm., Claudia Bucci, B.Sc.Pharm. Clinical Cardiovascular Pharmacist Pharmacy Department Sunnybrook and Women’s College Health Sciences Centre 2075 Bayview Avenue Toronto, Ontario M4N 3M5, Canada claudia.bucci@sw.ca. Search for other works by this author on: Oxford Academic Google Scholar Patti Madorin, B.Sc.Pharm. Patti Madorin, B.Sc.Pharm. Clinical Cardiovascular Pharmacist Pharmacy Department Sunnybrook and Women’s College Health Sciences Centre 2075 Bayview Avenue Toronto, Ontario M4N 3M5, Canada Search for other works by this author on: Oxford Academic Google Scholar American Journal of Health-System Pharmacy, Volume 60, Issue 19, 1 October 2003, Pages 1943–1946, https://doi.org/10.1093/ajhp/60.19.1943 Published: 01 October 2003
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".