Efficacy and Effectiveness of Clinical Trials Applied to the Treatment of Obesity: A Systematic Review
Bibliographic record
Abstract
The multidisciplinary treatment of obesity (MTO) has been recognized as the standard intervention and the basis for complementary treatments. However, it is still not clear how effective MTO programs can be and it has become more difficult to determine because normally the conditions of the intervention are not clearly stated. Therefore, it is important to make it clear if one MTO program is characterized as an efficacy trial, conducted under ideal conditions, or as an effectiveness trial developed in real-world scenarios. In this sense, this systematic review aims to explore some information from MTO studies. This systematic review made a search for potential papers using PubMed and SciELO, in which the terms efficacy or effectiveness were presented. After applying all the inclusion/exclusion criteria, four trials were included (343 participants). All the studies had characteristics of effectiveness trials. They used three different main outcomes, which made comparisons difficult. In conclusion, this review shows that a very small number of MTO studies have been published using the terminology efficacy or effectiveness. These studies do not explore the terms in a way that could make important distinctions among these distinct clinical trial models. Keywords: Obesity; Treatment; Efficacy; Effectiveness.
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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 | 0.030 | 0.134 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.010 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".