A comparison of virtual and in person delivery of a full meal replacement program for obesity
Bibliographic record
Abstract
Abstract Objective Full meal replacement (FMR) Intensive Lifestyle Interventions (ILI) have been used for weight management. However, predictors of efficacy with these programs are less clear. The primary objective was to assess weight loss predictors in a community‐based FMR ILI program. A secondary objective was to determine if weight loss was different between virtual and in person ILI. Methods This was a retrospective cohort study involving 234 patients who started the program between 1 January 2016 and 3 March 2021. In the 24‐week program, patients spent 12 weeks on FMR and then transitioned back to food for the remainder, with weekly follow up with a physician and group sessions with a dietitian. Visits were in person prior to March 2020 and virtual afterward. Results Patients' average age was 47.5 years (SD = 12.0) and 73.5% were female. Average weight loss was 14.3% (SD = 6.2%). There was no significant difference in weight loss between virtual and in person programs. Patients on a Glucagon‐like Peptide‐1 Receptor Agonist prior lost less weight. Other significant associations between groups were baseline Hemoglobin A1C, classes attended, as well as the age since peak weight. Conclusion Weight loss from virtual ILI was not significantly different from person ILI. More research is needed to determine how to best stratify care as virtual or in person using FMR programs.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".