COMMUNITY-BASED COMPREHENSIVE GERIATRIC ASSESSMENT CLINICAL DECISION ALGORITHMS AND PRACTICE GUIDELINES
Bibliographic record
Abstract
The Comprehensive Geriatric Assessment (CGA) has been suggested as the gold standard for the management of frailty in older people (British Geriatrics Society (BGS) 2014). It is a multi-dimensional process that requires expert clinical judgement and evidence-informed practices in order to gather, synthesize and interpret information of the client’s medical, social, psychological, physical and functional limitations to develop an integrated clinical profile and an individualized care plan (Kay, et al., 2017). Although the domains of the CGA are clearly documented, (Kay, et al., 2017), the intricacies of what to ask within the domains are not. The BGS practice guidelines for managing frailty suggest the need to develop local protocols and pathways of care to help guide assessment and treatment (BGS, 2014). The Regional Geriatric Program of Eastern Ontario’s Geriatric Assessment Outreach Team completed an evidence-based review to develop clinical decision algorithms for 15 domains within the CGA to support a practice that is effective and efficient. This poster will highlight the process and clinical outcomes of the 5-step evidence-based process that was used to develop a interview guide, decision algorithms and practice guidelines for 15 clinical topics for a CGA within a community context. These were developed to increase consistency amongst their interprofessional geriatric assessors and provide a practice framework that supported conscious competent clinical decision making. This poster will also highlight the challenges and opportunities of completing an evidence-based review with the provision of recommendations for other teams interested embarking on this type of clinical journey.
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 | 0.089 | 0.257 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.015 | 0.009 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.010 | 0.008 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.011 | 0.007 |
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".