Classification system for primary care provider eConsults about medications for older adults with frailty
Bibliographic record
Abstract
BACKGROUND: Providing primary care for people with frailty can be challenging due to an increased risk of adverse outcomes and use of potentially inappropriate medications which may exacerbate characteristics of frailty. eConsult is a service where primary care providers can receive timely specialist advice for their patients through a secure web-based application. We aimed to develop a classification system to characterize medication-focused eConsult questions for older adults with frailty and assess its usability. METHODS: A classification system was developed and refined over three cycles of improvement through a cross-sectional study of 35 cases categorized as medication-focused from cases submitted in 2019 for patients aged 65 or older with frailty through the Champlain BASE eConsult service (Ontario, Canada). The final classification system was then applied to each case. RESULTS: The classification system contains 5 sections: (1) case descriptives; (2) intent and type of question; (3) medication recommendations and additional information in the response; (4) medication classification; and (5) potentially inappropriate medications. Among the 35 medication-focused cases, the most common specialties consulted were endocrinology (9 cases, 26%) and cardiology (5 cases, 14%). Medication histories were available for 29 cases (83%). Many patients were prescribed potentially inappropriate medications based on explicit tools (AGS Beers Criteria®, STOPPFall, Anticholinergic Cognitive Burden Scale, ThinkCascades) yet few consults inquired about these medications. CONCLUSION: A classification system to describe medication-related eConsult cases for patients experiencing frailty was developed and applied to 35 eConsult cases. It can be applied to more cases to identify professional development opportunities and enhancements for eConsult services.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".