When Type 1 Diabetes Meets Dementia: Practical Strategies to Help Patients and Their Loved Ones
Bibliographic record
Abstract
Practical Pointers| October 18 2023 When Type 1 Diabetes Meets Dementia: Practical Strategies to Help Patients and Their Loved Ones Ian R. Blumer 0000-0002-0451-678X ; Ian R. Blumer 1University of Toronto, Temerty Faculty of Medicine, Toronto, Ontario, Canada Corresponding author: Ian R. Blumer, ian@ianblumer.com Search for other works by this author on: This Site PubMed Google Scholar Medha N. Munshi 0000-0001-6917-4197 ; Medha N. Munshi 2Joslin Geriatric Diabetes Programs, Beth Israel Deaconess Medical Center, Boston, MA3Harvard Medical School, Boston, MA Search for other works by this author on: This Site PubMed Google Scholar William H. Polonsky 0000-0001-9064-6144 William H. Polonsky 4Behavioral Diabetes Institute, San Diego, CA5University of California, San Diego, San Diego, CA Search for other works by this author on: This Site PubMed Google Scholar Corresponding author: Ian R. Blumer, ian@ianblumer.com Clin Diabetes cd230058 https://doi.org/10.2337/cd23-0058 Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Facebook Twitter LinkedIn Email Cite Icon Cite Get Permissions Citation Ian R. Blumer, Medha N. Munshi, William H. Polonsky; When Type 1 Diabetes Meets Dementia: Practical Strategies to Help Patients and Their Loved Ones. Clin Diabetes 2023; cd230058. https://doi.org/10.2337/cd23-0058 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAll JournalsClinical Diabetes Search Advanced Search This content is only available via PDF. ©2023 by the American Diabetes Association2023Readers may use this article as long as the work is properly cited, the use is educational and not for profit, and the work is not altered. More information is available at https://www.diabetesjournals.org/journals/pages/license. Article PDF first page preview Close Modal You do not currently have access to this content.
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 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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".