Industry and Academia Forge Strong Links to Train Veterinary Pathologists
Bibliographic record
Abstract
With continuing robust financial support of the biopharmaceutical industry, the Coalition for Veterinary Pathology Fellows has now established 13 new training positions for veterinary pathologists. This unique partnership between the American College of Veterinary Pathologists (ACVP) and Society of Toxicologic Pathology (STP) was established to provide a unified mechanism to solicit and allocate funding to train additional veterinary pathologists to fill critical positions in academia, government, and the private sector. Over the past 2 years, 51 applications have been submitted to the Coalition by North American training institutions for 13 newly funded training positions. Applications were evaluated by the Coalition Board of Governors and competitively awarded as follows. Appointed to anatomic pathology residencies are Drs. Torrie Crabbs, David Gardiner, and Sandeep Akare, funded by GlaxoSmithKline at the University of California at Davis, Colorado State University, and University of Illinois at Urbana-Champaign, respectively; Drs. Lyn Wancket, Robert Johnson, and Melissa Sanchez, funded by Pfizer at The Ohio State University, Purdue University, and University of Pennsylvania, respectively; Dr. Danielle Lewis Brown, funded by Sanofi-Aventis at North Carolina State University; and Dr. Jana Ritter, funded by Eli Lilly at Michigan State University. Appointed to postpathology residency PhD graduate positions are Drs. Alicia Olivier and Aaron Sargeant, funded by Bristol-Myers Squibb and Schering-Plough at Iowa State University and The Ohio State University, respectively. In addition, residents are currently being recruited for three other positions; two anatomic pathology residencies, funded by Amgen and Genentech at Washington State University and University of Guelph, respectively, and a clinical pathology residency funded by Merck at the University of Guelph. The Coalition has also received generous unrestricted grants from Bristol-Myers Squibb, Allergan, Experimental Pathology Labs, Integrated Lab Systems, Wyeth, The Leyden Group, and Burroughs Wellcome Fund to support important allied activities. Taken together, industry has provided more than $3M to support Coalition efforts. Solicitation of funds for additional training positions is underway for 2007. “Industry’s continued strong financial support and the intense competition amongst training institutions’ for these prized fellowships clearly demonstrates the value of, and need for, this type of collaborative educational initiative,” said Dr. Gary Cockerell, Director of the Coalition. Further information can be found on the ACVP/STP Coalition web site at 〈www.vetpathcoalition.org〉.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.023 | 0.044 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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; both teacher heads agree on what is shown here.
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".