Looking for a needle in a haystack – tackling rare diseases: an interview with Kym Boycott
Bibliographic record
Abstract
Kym Boycott is currently a Clinical Geneticist at the Children's Hospital of Eastern Ontario (CHEO) and a Senior Scientist at the CHEO Research Institute, in Canada, where she tries to better understand mechanisms of rare genetic diseases and improve the management of pediatric patients with these conditions. Her interest in Medical Genetics dates back to her undergraduate studies at Queen's University in Kingston, when she was captured by Dr Patrick MacLeod's lectures on this subject. Thus, she embarked on a PhD in Medical Genetics and joined Dr Torben Bech-Hansen's lab at the University of Calgary, where she investigated the cause of a rare genetic form of vision loss. After completion of her PhD, she attended the medical school program at the University of Calgary and obtained her MD in 2005. Having both a PhD and MD allowed her to have a translational perspective from the beginning of her career. At CHEO, Kym and her group aim to bridge basic and clinical knowledge to quickly diagnose – by using next-generation sequencing – and improve the management of rare diseases, also known as orphan diseases. Kym is co-leader of the Canadian Rare Diseases Models and Mechanisms (RDMM) project, the goal of which is to connect basic scientists who work with animal models to clinician investigators studying rare diseases, thereby catalyzing investigation of disease mechanism and in some instances facilitating therapeutic configuration for rare diseases. In this interview, Kym shares with us her unique experience and expertise, fighting on the front line against rare diseases.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".