Telemedicine for Patients With Systemic Lupus Erythematosus in a Publicly Funded Hospital System: Retrospective Study
Bibliographic record
Abstract
BACKGROUND: Systemic lupus erythematosus (SLE) is a chronic autoimmune disease that requires frequent clinic and laboratory visits. However, patients with SLE, particularly those who are underresourced, have unacceptably high rates of no-shows. OBJECTIVE: This study aims to determine no-show rates associated with telemedicine visits during the COVID-19 pandemic in comparison to no-show rates associated with contemporaneous and historic in-person visits. METHODS: We performed a retrospective cohort study in a publicly funded county hospital system in Houston, Texas. We identified a cohort of established patients with SLE by the International Classification of Diseases codes that were independently confirmed as SLE by a review of medical records. We identified patients who were seen from March to December in 2018, 2019, and 2020 (to reflect the height of the COVID-19 pandemic and account for seasonal changes in disease activity). Our primary outcome was the percentage of no-shows for rheumatology clinic appointments. Our secondary outcome was laboratory use adherence, which was defined as lupus-specific blood and urine studies conducted within 30 days of the scheduled appointment. Covariates included age, sex, race, ethnicity, and SLE-related prescription drugs. RESULTS: We included 156 patients with SLE in our analysis. Most were female (n=141, 90.4%), were Hispanic (n=75, 49.3%), and had a median age of 43 (range 19-80) years. In 2020, the no-show rate for telemedicine was 5.5% (10/182) compared to a no-show rate of 16.2% (31/191) for in-person visits (P=.002). After multivariable adjustment for covariates, the odds of no-show were lower for telemedicine visits (odds ratio 0.39, 95% CI 0.20-0.77). There were no differences in adherence to laboratory testing. CONCLUSIONS: Telemedicine visits had decreased odds of no-shows without difference in laboratory testing adherence after adjustment for covariates. More research is needed to determine the clinical impact of telemedicine on patients with SLE.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".