Applying QI-focused SPIDER approach to safer deprescribing for geriatric patients: Results of the toronto feasibility study
Bibliographic record
Abstract
Context: More than 25% of Canadian seniors are prescribed 10+ different medications each year. There is a direct association between more medications and persistent high care needs/costs for seniors. Effective and appropriate deprescribing for seniors in primary care is needed. Objective: To present results of a feasibility study of the Structured Process Informed by Data, Evidence and Research (SPIDER) project aiming at improving safer deprescribing for complex older patients in community-based primary care. Study Design: Single-arm mixed methods study in Toronto, Ontario. Evaluation included participant interviews, focus groups, field notes and quantitative EMR data. Setting: Primary care practices affiliated with the University of Toronto Practice-Based Research Network (UTOPIAN). Population Studied: Patients aged 65+ years taking 10+ different medications. Intervention: 1) QI-focused Learning Collaboratives (LCs); 2) practice coaching; and 3) EMR data for audit & feedback. Outcome Measures: feasibility across eight dimensions: acceptability, demand, implementation, adaptation, integration, practicality, and efficacy. Results: Demand: 33 physicians and 24 allied health professionals from ten UTOPIAN practices and one community health centre participated in the Toronto LC. Implementation: the LC included a full day initial workshop, two short webinars, and a half-day summative congress over nine months. Practices had a monthly call with their QI coach and quarterly data reports. Adaptation, integration, practicality: teams developed various deprescribing tools and processes that were integratable to local context and existing practices. Acceptability: Teams perceived access to coaching as a valuable element of the approach. The initial review and validation of patients identified in the data reports were deemed time-consuming, particularly for under-resourced practices with a large cohort of target patient population. Access to pharmacist services and in-house QI and data support was considered two critical enablers to the sustainability of the approach. Efficacy: reductions in PIP prevalence and prevalence of patients with at least one PIP were 3.6% (p=.4) and 1.4% (p=.5), respectively. Conclusions: The SPIDER approach appears to be feasible. Access to coaching support and pharmacist services may enhance sustainability.
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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 | 0.012 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".