Juvenile diabetes: Understanding its impact beyond the pancreas
Bibliographic record
Abstract
My name is Lawrence Yau and I am in my 5th year doing an Honours Specialization in Medical Sciences. My decision to pursue this degree was based on my interest in learning more about human diseases. Although I gained a lot of knowledge through my studies, it was a challenge to put a face on the diseases that I learned at school. Consequently, I started volunteering at Rotoract's Juvenile Diabetes Camp (JD Camp) three years ago. JD Camp is a weekend camp operating during the month of March and is open to both children and families affected by Type I diabetes. The camp experience offers a host of fun activities for the children and also provides many networking and educational opportunities for parents. As the food coordinator, in addition to planning and preparing meals for roughly 60 people each year, I had the opportunity to interact with both the children and their parents. Through my experiences at the camp, I gained a greater appreciation and understanding of not only the physical implication of Type I diabetes on the child but also its impact on the emotional, social, and financial well being of the entire family. I believe the challenge facing future medical research lies in improving the quality of life of patients afflicted with Type I diabetes and their families.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".