How can information science contribute to alzheimer's disease research?
Bibliographic record
Abstract
ABSTRACT There are many indicators about the worldwide rise of age‐related neurodegenerative diseases, especially Alzheimer's Disease (AD). Significant and urgent improvements are needed in the areas of prevention, drug discovery, treatment, cost‐effective delivery of health‐care service, and caregiver support. Researchers and professionals from medicine, nursing, health care, computer science, economics and other disciplines have intensified interdisciplinary efforts to address this looming global crisis. To‐date, Information Science has been peripheral to these collaborative developments, contributing primarily in traditional areas of user information needs and services. The presenters in this interactive panel will challenge participants with a simple but provocative question: “How can Information Science (IS) contribute to research on AD?” To inspire the generation of ideas for connections among existing streams in IS research and questions critical to AD, the panelists will describe briefly their own research in AD or related areas, and speak to their potential to advance research in AD. The audience will then engage in small group discussions for rapid idea generation, exchange and exploration. The discussions will be structured around interdisciplinary research areas developed at the University of Pennsylvania's Ware Alzheimer Program Model: 1. Drug Discovery, 2. Biomarkers, 3. Recruitment, Retention and Outcomes, 4. Care Coordination. The objective is to discuss: the potential for Information Science to contribute in the areas of research identified in the Model; unique perspectives (methods, theoretical frameworks, service models) that IS could provide; and potential research partnerships that could be established within and outside of IS. The session will conclude with a collective development of priorities for Information Science engagement in AD research. The participants will be encouraged to become instigators of an IS focus on AD research in their own disciplinary areas, and to contribute to the development of a collaborative virtual space for continuing discussion of these priorities.
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 | 0.004 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".