From research to bedside: Incorporation of a <scp>CGA</scp> ‐based frailty index among multiple comanagement services
Bibliographic record
Abstract
The comprehensive geriatric assessment (CGA) is the core tool used by geriatricians across diverse clinical settings to identify vulnerabilities and estimate physiologic reserve in older adults. In this paper, we demonstrate the iterative process at our institution to identify and develop a feasible, acceptable, and sustainable bedside CGA-based frailty index tool (FI-CGA) that not only quantifies and grades frailty but also provides a uniform way to efficiently communicate complex geriatric concepts such as reserve and vulnerability with other teams. We describe our incorporation of the FI-CGA into the electronic health record (EHR) and dissemination among clinical services. We demonstrate that an increasing number of patients have documented FI-CGA in their initial assessment from 2018 to 2020, while additional comanagement services were established (Figure 2). The acceptability and sustainability of the FI-CGA, and its routine use by geriatricians in our division, were demonstrated by a survey where the majority of clinicians report using the FI-CGA when assessing a new patient and that the FI-CGA informs their clinical management. Finally, we demonstrate how we refined and updated the FI-CGA, we provide examples of applications of the FI-CGA across the institution and describe areas of ongoing process improvement and challenges for the use of this tailored yet standardized tool across diverse inpatient and outpatient services. The process outlined can be used by other geriatric departments to introduce and incorporate an FI-CGA.
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 | 0.120 | 0.189 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".