Bibliographic record
Abstract
As Clinical Research Nurse and the Project Manager for the Department of Surgery, Jamie Jay Rothstein builds systems for implementing and managing all aspects of the Department’s clinical trials – from protocol development to fiscal management. She also guides the Department’s research coordinators, interacting with them regularly for ongoing education and training. Before joining Jefferson in July 2013, Rothstein created, developed and managed a research department of 10 physicians. Through strategic planning, analysis, business plan development, quality and performance improvement, the department grew to a staff of several coordinators. She says that while she enjoyed the position, she jumped at the chance to move from pharmaceutical to academic research. “I love having the opportunity to work with the physicians and research coordinators at Jefferson,” she says. “Their passion for research – and compassion for patients – is unparalleled.” Rothstein has been a nurse since 1991, when she was the first Graduate Nurse hired into the Cardiac Intensive Care Unit at the Children’s Hospital of Philadelphia. By 1995, she was serving as Primary Nurse and Clinical Research Nurse Coordinator at CHOP. Over the past two decades, she has worked in a variety of clinical nursing fields and clinical research positions, including serving as a nurse educator for the OR in the Same Day Surgery Unit at several hospitals and surgical centers. In addition to being a Certified Clinical Research Coordinator, she holds certifications in Good Clinical Practice from the National Institutes of Health and is a member of SOCRA (Society of Clinical Research Associates) and ACRP (Association of Clinical Research Professionals). Rothstein lauds the quality of research underway at Jefferson: “We’re doing innovative and groundbreaking work, such as studies of immunotherapy for specific kinds of cancer and growth of cell lines. The physicians are committed to the studies’ success.” She lives with her husband in Cherry Hill, NJ, and enjoys being a mother to two daughters—and three Newfoundland dogs.
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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 | 0.002 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.106 | 0.023 |
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".