GLP-1 receptor agonist therapy for obesity via direct-to-consumer telemedicine: Clinical characteristics and treatment outcomes
Bibliographic record
Abstract
Objective: This study aimed to characterize the patient population using a DTC telemedicine platform for obesity treatment with liraglutide, evaluate treatment success and adherence, and assess the side effect profile using patient-reported outcomes. Methods: We conducted a retrospective cross-sectional study using anonymized data from 966 patients who received liraglutide prescriptions through a DTC platform between August 2022 and April 2024. Patients completed an initial online questionnaire to assess eligibility, followed by a physician's review. A follow-up questionnaire was administered 50 days after the first prescription to evaluate outcomes, including weight loss, adverse events, and treatment satisfaction. Results: The majority of patients (70%) had long-standing obesity, with 46.6% having a BMI between 30 and 34.4 kg/m². Most (88.9%) were new to glucagon-like peptide-1 receptor agonists therapy. After 50 days, 85.6% of patients reported a weight loss of more than 2 kg, with an average loss of 4.9 kg. Adverse events were reported by 39.8% of patients, primarily gastrointestinal issues. Treatment adherence was high, with 94.1% following the prescribed regimen. Despite adverse events, 86.4% of patients expressed a desire to continue treatment. Conclusion: This study demonstrates the potential effectiveness and accessibility of DTC telemedicine for obesity treatment using liraglutide, though gastrointestinal side effects were common. The findings support the use of DTC platforms for weight management, yet further research with longer follow-up and professional evaluation is necessary to confirm long-term safety and efficacy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".