Geriatric Fast Facts: Four Years of Growth and Reach
Bibliographic record
Abstract
Abstract Clinical teachers are increasingly challenged to find the time to provide point of care education for learners. To meet this challenge, an interprofessional team from competing health care systems created and sustained Geriatric Fast Facts (GFF): easily accessible, concise 1-2 page topic summaries for clinical teachers to use (in lieu of the mini-lecture) with learners at the point of care. Designed by geriatrics educators in consultation with IT experts, we launched a mobile enabled website that is indexed and searchable by free text or topic, organ system, ACGME competency, disease, and the “underlying science” for the disease/illness. GFF topics are authored by subject matter experts with peer review by senior geriatricians. Brief quizzes test learners’ knowledge with score reports. Our 4-year results reveal that: 1) “Geriatric Fast Facts” appear in the top Google 10 listing; 2) Search engines account for 39% of site traffic; 3) 60% of unique users (N=29,000) are via direct link; 4) 19% via referral from another site. Our “bounce rate” (55%) is ideal as users quickly gain the information sought - affirmed by our session duration which averages < 2 minutes. Our Google Analytics results reflect steady growth and international reach: 73% of sessions are from the U.S., 5% from Canada, 22% international and growing! In summary with our population continuing to age and time for teaching being limited, GFFs are quick, accessible, evidence-based resources for point of care teaching.
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 | 0.007 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 0.006 |
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".