Evaluation of a Virtual Pre-Consultation Tool for Older Adults in Primary Care: Results from a Randomized Trial
Bibliographic record
Abstract
Context: Virtual pre-consultation screening of patient needs may offer opportunities to improve the care and health outcomes of older patients in primary care, especially those with multiple care needs. Objective: We sought to implement and evaluate the effectiveness of a multidimensional virtual pre-consultation tool in the primary care setting to support rapid and standardized needs assessment for older persons. Study Design and Analysis: Pragmatic, multi-center, 1:1 individually randomized trial design. Implementation was conducted using a participatory approach over a 3-month period. Baseline and 3-month follow-up data were collected through phone-based questionnaires. An intention-to-treat analysis was carried out. Setting: Four university-affiliated interprofessional primary care clinics, two clinics in one urban region (Montreal) and two in one rural region (Abitibi) in Quebec, Canada. Population Studied: Patients 65 years and older with a consultation with a primary care provider (physician, nurse, social worker, other) during the implementation period in one of the participating clinics. Intervention: A virtual pre-consultation tool, ESOGER, was administered as a phone-based questionnaire by a member of the clinic staff to eligible patients prior to their consultation with the primary care provider. The ESOGER tool provides a general assessment of the physical, social, mental and cognitive health needs of older adults and produces a summary report available to clinicians at the time of consultation. Outcome Measures: The primary endpoint consisted of the EQ-5D quality of life score at 3-month follow-up. Secondary endpoints were unplanned primary care visits, visits to the ED and hospital admissions in last 3 months. Results: Of the 659 eligible patients contacted to date, 345 (52.3%) agreed to participate and have been randomized. Follow-up assessments are ongoing with a loss to follow-up of 22.8% and will be completed by August 2022. Final results of the intention-to-treat analysis will be presented overall and stratified by urban and rural sites. Conclusions: Intended consequences of this intervention include an increased responsiveness of consultations for providers resulting in improved care of older patients. Overall, we hope results will support the implementation of evidence-based, multidimensional and virtual pre-consultation tools for older persons in the primary care setting.
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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 | 0.015 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".