Metabolically Healthy Overweight and Obesity
Bibliographic record
Abstract
Letters1 April 2014Metabolically Healthy Overweight and ObesityCaroline K. Kramer, MD, PhD, Bernard Zinman, CM, MD, and Ravi Retnakaran, MDCaroline K. Kramer, MD, PhDFrom Mount Sinai Hospital, University of Toronto, Toronto, Ontario, Canada.Search for more papers by this author, Bernard Zinman, CM, MDFrom Mount Sinai Hospital, University of Toronto, Toronto, Ontario, Canada.Search for more papers by this author, and Ravi Retnakaran, MDFrom Mount Sinai Hospital, University of Toronto, Toronto, Ontario, Canada.Search for more papers by this authorAuthor, Article, and Disclosure Informationhttps://doi.org/10.7326/L14-5007-7 SectionsAboutFull TextPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinkedInRedditEmail IN RESPONSEOne reason to do a meta-analysis is to confirm the findings observed in smaller individual studies and document the magnitude of effect in a larger study population to enable a more accurate estimate of effect. This approach is particularly important when evaluating a low- or moderate-risk population (such as metabolically healthy obese persons) because it enhances statistical power to detect differences in outcomes that individual studies could not identify. However, we recognize that the pooling of several studies requires that their data be relatively homogeneous, which partly limits the questions that a single meta-analysis can answer.Drs. Chaput ...References1. Kuk JL, Ardern CI, Church TS, Sharma AM, Padwal R, Sui X, et al. Edmonton Obesity Staging System: association with weight history and mortality risk. Appl Physiol Nutr Metab. 2011;36:570-6. [PMID: 21838602] CrossrefMedlineGoogle Scholar2. Hardy RJ, Thompson SG. A likelihood approach to meta-analysis with random effects. Stat Med. 1996;15:619-29. [PMID: 8731004] CrossrefMedlineGoogle Scholar Author, Article, and Disclosure InformationAuthors: Caroline K. Kramer, MD, PhD; Bernard Zinman, CM, MD; Ravi Retnakaran, MDAffiliations: From Mount Sinai Hospital, University of Toronto, Toronto, Ontario, Canada.Disclosures: None disclosed. Forms can be viewed at www.acponline.org/authors/icmje/ConflictOfInterestForms.do?msNum=M13-1059. PreviousarticleNextarticle Advertisement FiguresReferencesRelatedDetailsSee AlsoAre Metabolically Healthy Overweight and Obesity Benign Conditions? Caroline K. Kramer , Bernard Zinman , and Ravi Retnakaran Metabolically Healthy Overweight and Obesity Jean-Philippe Chaput and Arya M. Sharma Metabolically Healthy Overweight and Obesity Dorit Samocha-Bonet , Antony D. Karelis , and Rémi Rabasa-Lhoret Metabolically Healthy Overweight and Obesity Nathalie Esser , André J. Scheen , and Nicolas Paquot Metabolically Healthy Overweight and Obesity Gerson T. Lesser Metabolically Healthy Overweight and Obesity Juhee Cho , Yoosoo Chang , and Seungho Ryu Metabolically Healthy Overweight and Obesity Katherine M. Flegal Metrics Cited byObesity treatment: Weight loss versus increasing fitness and physical activity for reducing health risksMetabolic health and weight: Understanding metabolically unhealthy normal weight or metabolically healthy obese patients 1 April 2014Volume 160, Issue 7Page: 516KeywordsFactor analysisHealth statisticsHigh density lipoprotein cholesterolInflammationMortalityObesityOverweightPhenotypesStatistical methodsSystematic reviews ePublished: 1 April 2014 Issue Published: 1 April 2014 Copyright & PermissionsCopyright © 2014 by American College of Physicians. All Rights Reserved.PDF downloadLoading ...
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.041 | 0.007 |
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".