Decision-making in the management of obesity: a scoping review protocol
Bibliographic record
Abstract
OBJECTIVE: This scoping review will evaluate the current published literature on decision-making in obesity management. INTRODUCTION: Obesity is increasing in incidence worldwide. Although indications have been established for a variety of available treatment modalities, treatment selection must also factor in patient preferences, clinician expertise, and resource availability. Such considerations are crucial given the exponential expansion of new surgical techniques and pharmacologic options in the last decade. Although literature exists for decision-making on various obesity management topics, there are no scoping reviews systematically mapping the literature. This scoping review is timely given that the treatment of obesity has evolved into a multidisciplinary endeavor with myriad management decisions that both patients and clinicians must navigate. INCLUSION CRITERIA: The review will consider for inclusion full-text primary studies, published in English from the year 2000 onwards, pertaining to decision-making in obesity management for health care providers involved in obesity management for patients aged ≥18 years. METHODS: This scoping review will be conducted in accordance with the JBI methodology for scoping reviews. Embase (Elsevier), MEDLINE (PubMed), Scopus (Elsevier), Web of Science (Clarivate), CINAHL Complete (EBSCO), PsycINFO (EBSCO), and Cochrane Central (Wiley) will be systematically searched using a predefined strategy. Two independent reviewers will conduct a 3-tiered screen of identified articles, with a third reviewer resolving disputes. Data extraction will be performed using a predefined, yet flexible form. Descriptive summaries and mapping will be provided for included studies. Available evidence and knowledge gaps will be identified and summarized as they relate to specific concepts, populations, and contexts in obesity management decision-making.
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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 | 0.175 | 0.128 |
| Meta-epidemiology (narrow) | 0.005 | 0.006 |
| Meta-epidemiology (broad) | 0.012 | 0.014 |
| Bibliometrics | 0.021 | 0.017 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.008 | 0.010 |
| Research integrity | 0.011 | 0.009 |
| Insufficient payload (model declined to judge) | 0.080 | 0.020 |
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".