McMaster Optimal Aging Portal: an evidence-based database for geriatrics-focused health professionals
Bibliographic record
Abstract
OBJECTIVE: The objective of this work was to provide easy access to reliable health information based on good quality research that will help health care professionals to learn what works best for seniors to stay as healthy as possible, manage health conditions and build supportive health systems. This will help meet the demands of our aging population that clinicians provide high quality care for older adults, that public health professionals deliver disease prevention and health promotion strategies across the life span, and that policymakers address the economic and social need to create a robust health system and a healthy society for all ages. RESULTS: The McMaster Optimal Aging Portal's (Portal) professional bibliographic database contains high quality scientific evidence about optimal aging specifically targeted to clinicians, public health professionals and policymakers. The database content comes from three information services: McMaster Premium LiteratUre Service (MacPLUS™), Health Evidence™ and Health Systems Evidence. The Portal is continually updated, freely accessible online, easily searchable, and provides email-based alerts when new records are added. The database is being continually assessed for value, usability and use. A number of improvements are planned, including French language translation of content, increased linkages between related records within the Portal database, and inclusion of additional types of content. While this article focuses on the professional database, the Portal also houses resources for patients, caregivers and the general public, which may also be of interest to geriatric practitioners and researchers.
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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 | 0.009 | 0.113 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.064 | 0.066 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.160 | 0.025 |
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".