A Discussion with Dr. Natasha Kekre, Hematologist and Clinician Scientist
Bibliographic record
Abstract
[Figure: see text] Dr. Natasha Kekre has been appointed to the Department of Medicine in the Division of Hematology, within the Transplant and Cellular Therapy Program at The Ottawa Hospital since 2015. She is also a scientist within the Ottawa Hospital Research Institute and an associate professor of medicine at the University of Ottawa. She completed her Bachelor's in Science at the University of Windsor then obtained her medical degree from the University of Ottawa. She trained at the University of Ottawa in Internal Medicine and Hematology, then did fellowship in stem cell transplantation at Dana Farber Cancer Institute in Boston, MA with a Masters in Public Health from Harvard University. Her research is focused on developing early phase clinical trials and moving home grown therapeutic strategies from the lab to patients in the clinic. She has collaborated with scientists and physicians across Canada to build a Canadian CAR-T cell platform (chimeric antigen receptor T cells are immune cells engineered to kill cancer cells), bringing this exciting new therapy to Canadian patients.
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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 | 0.006 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.016 | 0.023 |
| Insufficient payload (model declined to judge) | 0.060 | 0.033 |
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".