Evaluation of a pre-consultation tool for older adults in primary care: Results from a randomized controlled trial
Bibliographic record
Abstract
Context: Pre-consultation questionnaires designed to provide rapid assessments of the physical, social, mental and cognitive health of older adults may support effective primary care management and improved patient outcomes for this population. Objective: This pilot study sought to evaluate the effectiveness of ESOGER (Socio-Geriatric Evaluation) as a pre-consultation tool in improving patient outcomes for older adults in primary care as compared to usual care. Study Design and Analysis: Multi-center, 1:1 individually randomized trial design. Implementation was conducted 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 (ClinicalTrials.gov#NCT05102890). 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 an upcoming consultation with a primary care provider in one of the participating clinics were considered eligible. Intervention: We randomly allocated eligible participants to be administered the ESOGER questionnaire prior to their consultation or to receive usual care. For participants in the intervention group, an automatically-generated summary report of ESOGER was placed in their electronic chart prior to consultation. Outcome Measures: The primary endpoint consisted of the difference in the EQ-5D health-related quality of life score at 3-month follow-up. Secondary endpoints were visits to the emergency department and hospitalizations in last 3 months. Results: Participant mean age was 74.7, 58.4% were women and 75% completed the follow-up assessment. The 3-month change in EQ-5D was 10% higher in the intervention group than in the control group but was not statistically significant (OR [95%CI]: 1.1 [0.9, 1.4]). No significant change in visits to the emergency department (OR = 1.33, 95% CI [0.62, 2.83]) or hospitalizations (OR = 2.36, 95% CI [0.75, 7.90]) in the intervention vs control group were observed. Conclusions: No improvement in patient outcomes was found 3 months following the implementation of the ESOGER tool. Future work will report on patient and provider perspectives as well as barriers and facilitators to implementation in the current primary care context.
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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 | 0.009 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".