Bibliographic record
Abstract
TRIBUTEA Tribute to Charlie Bayliff T he hospital pharmacy community was shocked and saddened to learn that Charlie Bayliff died on Sunday, January 24, 2010.I was privileged to work with Charlie in a variety of ways over the years.I first met Charlie in the early '80s when he joined Sunnybrook Medical Centre as a clinical coordinator before moving on to what is now called the London Health Sciences Centre.We worked together again when Charlie was an Associate Editor for the CJHP, from October 1992 to May 1998.Charlie was an easy-going, personable individual, always ready with a joke or humorous anecdote.His enthusiasm, wide-eyed grin, and infectious smile made him someone you wanted to spend time with.However, it was his dedication and commitment to pharmacy that set him apart and made him an important influence on the profession in Canada.He was a member of the CSHP for more than 25 years, was awarded a CSHP Fellowship in 1996, and was winner of the Society's Distinguished Service Award in 2001.Charlie once told me he was an "ideas man".Combined with his enthusiasm and sound logic, his ideas were infectious.Beyond patient care, his passion focused on teaching and training.In this, he served as a role model for us all.In London, Charlie was heavily involved in the hospital pharmacy residency program, and he served as a preceptor for PharmD rotations for students from the University of Toronto.Charlie encouraged his residents and PharmD students to publish case reports as a way of meshing clinical training and research.1 His encouragement in this area inadvertently led to Charlie likely coauthoring more case reports
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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 | 0.010 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.012 | 0.006 |
| Scholarly communication | 0.013 | 0.004 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.043 | 0.052 |
| Insufficient payload (model declined to judge) | 0.048 | 0.037 |
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".