Inter‐rater Reliability of Clinical Frailty Scores for Older Patients in the Emergency Department
Bibliographic record
Abstract
O ver 50 million U.S. adults 65 years and older account for >20 million emergency department (ED) visits each year. 1 Increasing ED use by older adults is projected to exceed the capacity of U.S. EDs. 2 The traditional ED model of care is ill-equipped to address the many complex care needs of older adults. 2 To address these problems, an evolution of emergency care has developed as evidenced by consensus geriatric ED (GED) guidelines and the American College of Emergency Medicine's Geriatric Emergency Department Accreditation (GEDA) program (https://www.acep.org/geda/).3 Although the GED guidelines recommend "routine screening for all geriatric patients for high-risk features," there are no screening tests that effectively predict poor outcomes among older patients who seek ED care.4 The currently used screening tests measure risk of a composite of poor outcomes such as functional decline, death, and depression after an ED visit.Alternatively, frailty might be used to identify older adults who might benefit from specialized GED care.Frailty is a state of heightened vulnerability to stressors arising from impairments in multiple systems leading to declines in homeostatic reserve and resiliency.Frailty predicts poor outcomes for older adults after acute care.However, it is not a concept familiar to many ED clinicians.
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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 | 0.024 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".