Ask the Experts: Pediatric Type 1 diabetes: adjunctive therapies, celiac disease and the role of the primary care physician
Bibliographic record
Abstract
Paola Luca is a clinical fellow in pediatric endocrinology and metabolism at The Hospital for Sick Children (ON, Canada). She obtained her medical degree at the University of Toronto (ON, Canada) and completed her training in pediatrics at The Hospital for Sick Children. She is currently pursuing a Master of Science through the Institute of Medicine at the University of Toronto. Her research focus is evaluating clinical outcomes of adolescents with severe obesity enrolled in the interdisciplinary weight management program at The Hospital for Sick Children. Jill Hamilton is a pediatric endocrinologist at The Hospital for Sick Children, Medical Director of the Sick Kids Team Obesity Management Program (STOMP), Senior Associate Scientist and Associate Professor of Pediatrics at the University of Toronto. Her clinical work is in endocrinology and diabetes, with a particular focus in the area of obesity. She completed her medical school training at the University of Ottawa (ON, Canada) and then trained at The ...
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.007 | 0.004 |
| Insufficient payload (model declined to judge) | 0.028 | 0.004 |
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".