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Record W4308653736 · doi:10.5867/medwave.2022.10.2649

Obesity in adults: Clinical practice guideline adapted for Chile

2022· review· en· W4308653736 on OpenAlexaboutno aff

Bibliographic record

VenueMedwave · 2022
Typereview
Languageen
FieldMedicine
TopicBariatric Surgery and Outcomes
Canadian institutionsnot available
Fundersnot available
KeywordsGuidelineMedicineObesityWeight managementClinical PracticeHealth careManagement of obesityWeight lossMEDLINESystematic reviewFamily medicineNursingPolitical science

Abstract

fetched live from OpenAlex

Introduction: The Chilean Society of Bariatric and Metabolic Surgery, together with other scientific societies, led a process for adapting the Canadian clinical practice guideline for obesity in adults for Chile. The aim of the Canadian guideline, among its main objectives, was to propose changes in obesity management using a chronic disease framework and focusing on improving patient-centered health outcomes, rather than focusing on weight loss alone. Methods: A group of 58 healthcare professionals applied the GRADE-Adolopment method to analyze and adapt the original recommendations and to create de novo recommendations. New recommendations were developed through a systematic review of the evidence using the Epistemonikos database and based on the GRADE-Evidence to Decision (EtD) framework. Results: Seventy-six (76) of the 80 original recommendations were adopted, one recommendation was adapted, and 12 new recommendations were created. Conclusions: The adaptation process reduced the time needed to develop a Chilean clinical practice guideline for the management of obesity in adults. The change in obesity management approaches towards non-stigmatizing and patient-centered strategies focused on improving health outcomes and not solely on weight reduction is universal and it is possible to apply this approach in different countries and contexts.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.014
metaresearch head score (Gemma)0.039
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.076
Threshold uncertainty score0.152

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.039
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.003
Bibliometrics0.0080.007
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0030.002
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0060.002

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.

Opus teacher head0.128
GPT teacher head0.444
Teacher spread0.316 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations22
Published2022
Admission routes1
Has abstractyes

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