Comparison of in-person and telegeriatric follow-up consultations
Bibliographic record
Abstract
Introduction Telehealth has the potential to support the care of older adults and their desire to age at home by providing a videoconferencing connection to specialist geriatric care. However, more information is needed to determine how telehealth services affect the care of older adults, and how telehealth services for older adults compare to traditional in-person methods of care provision. The aim of this study was to compare telegeriatric and in-person geriatric consultation methods with respect to outcomes and costs. Methods This was a retrospective chart analysis of consultation letters from patients’ first follow-up appointment with a geriatric specialist during the 2017/2018 fiscal year ( N = 95) in a health jurisdiction of a Western Canadian province. Results Patients seen through telehealth and in person were similar in mean age ( M = 79.1 and 78.1 years, respectively) and were predominately female. Telegeriatric consultations resulted in more requests for further testing and screening ( p = 0.003), new diagnoses ( p = 0.002), medication changes ( p = 0.009) and requests for follow-up ( p = 0.03) compared to in-person consultations. An average one-day clinic with one geriatric specialist providing consultations through telehealth cost Can$1684–$1859 less than an equivalent in-person clinic. Discussion Although additional research is needed to explain the differences in outcomes further between telehealth and in-person consultations found in this work, telehealth consultations cost substantially less than in-person consultations and are a promising way to improve access to geriatric care for older adults in underserved areas.
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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 | 0.002 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".