The Development of Geriatric Assessment and Intervention Guidelines for an Online Geriatric Assessment Tool: A Canadian Modified Delphi Panel Study
Bibliographic record
Abstract
BACKGROUND: There are no guidelines available for what assessment tools to use in a patient's self-completed online geriatric assessment (GA) with management recommendations. Therefore, we used a modified Delphi approach with Canadian expert clinicians to develop a consensus online GA plus recommendations tool. METHODS: The panel consisted of experts in geriatrics, oncology, nursing, and pharmacy. Experts were asked to rate the importance and feasibility of assessments and interventions to be included in an online GA for patients. The items included in the first round were based on guidelines for in-person GA and literature review. The first two rounds were conducted using an online survey. A virtual 2 h meeting was held to discuss the items where no consensus was reached and then voted on in the final round. RESULTS: 34 experts were invited, and 32 agreed to participate. In round 1, there were 85 items; in round 2, 50 items; and in round 3, 25 items. The final tool consists of fall history, assistive device use, weight loss, medication review, need help taking medication, social supports, depressive symptoms, self-reported vision and hearing, and current smoking status and alcohol use. CONCLUSION: This first multidisciplinary consensus on online GA will benefit research and clinical care for older adults with cancer.
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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 | 0.199 | 0.157 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".