Opportunity costs of attending surgical clinic appointments and experiences with telemedicine for follow-up care
Bibliographic record
Abstract
OBJECTIVES: Telemedicine has been rapidly implemented in orthopedics during the coronavirus (COVID-19) pandemic. The purpose of this study was to quantify opportunity costs for patients attending typical in-person appointments and understand their perceptions of telemedicine for follow-up care. METHODS: A cross-sectional study was performed by surveying patients who had elective orthopedic surgery and attended at least one in-person and one phone call appointment. The survey assessed opportunity costs associated with in-person appointments, experience with telemedicine, and preferred type of future appointment. RESULTS: Of the 49 eligible patients, 41 (83.7%) completed the survey. The median travel distance to the clinic was 108 km, and the time spent in the clinic was 60 min. Participants responded "yes" to various forms of opportunity costs associated with attending in-person appointments, including missed work (46.3%), lost income (34.1%), recreational activities (26.8%), home or yard care (14.6%), socializing with friends or family (12.2%), school (2.4%), and childcare (2.4%). In addition, elements of the telemedicine appointment were rated from 1 (least favorable) to 10 (most favorable), and averages were calculated for ease of use (9.2), convenience (8.4), confidence in the doctor's diagnostic ability (8.2), likelihood of using the service again (6.4), and overall satisfaction (8.2). Preferred future appointment types included having the first visit in-person and subsequent visits via telephone (61.0%), in-person only (36.6%), and unsure (2.4%). CONCLUSION: This study identifies various opportunity costs associated with in-person orthopedic appointments and a favorable view toward telemedicine for follow-up care.
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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 | 0.001 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".