Brand Name Ingredients Retail price
Bibliographic record
Abstract
The information in this document is not a substitute for clinical judgment in the care of a particular patient. CADTH is not liable for any damages arising from the use or misuse of any information contained in, or implied by, the information in this presentation. The statements, conclusions, and views expressed herein do not necessarily represent the view of Health Canada, or any Provincial or Territorial Government. Made possible through funding from Health Canada. Copyright © 2009 CADTHCase 1 – Type 1 diabetes in an adolescent AJ is a 13-year-old (weight = 35kg) male recently diagnosed with type 1 diabetes. His mother gives you a prescription for: Glargine (Lantus®) – four units at bedtime, increase by one unit at bedtime, until morning blood glucose ~ 7 mmol/L M: 5 x 3mL, R: 3 Aspart (NovoRapid) – four units three times a day before meals, increase as directed M: 5 x 3mL, R: 3 AJ’s mother explains she does not have a drug plan and wonders if there is a less expensive and just as effective insulin available? Question: What would you tell AJ’s mother? You may want to discuss the basic differences between the insulin analogues and human insulin with AJ’s mother (e.g., onset of action, peak, and duration of activity) and review the costs of each product (refer to chart below).
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.752 | 0.712 |
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".