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Record W2952752490

Normal-weight central obesity: Unique hazard of the toxic waist.

2019· article· en· W2952752490 on OpenAlexaff
N. John Bosomworth

Bibliographic record

VenuePubMed · 2019
Typearticle
Languageen
FieldMedicine
TopicBariatric Surgery and Outcomes
Canadian institutionsSpinal Cord Injury BCUniversity of British Columbia
Fundersnot available
KeywordsMedicineOverweightBody mass indexObesityWaistAbdominal obesityHazard ratioWeight lossDyslipidemiaWaist-to-height ratioWaist–hip ratioInternal medicineProspective cohort studyPhysical therapyConfidence interval
DOInot available

Abstract

fetched live from OpenAlex

OBJECTIVE: To examine the mortality risk presented by normal-weight central obesity, to identify a clinical measure to aid in the identification of this phenotype, and to explore the means for mitigation of this risk. QUALITY OF EVIDENCE: Only prospective cohort studies (level II) comparing participants with central obesity at normal weight with those at higher levels of body mass index (BMI) were found. Good level I studies were available to demonstrate the effect of diet and exercise interventions on central obesity and mortality. MAIN MESSAGE: Participants with atherogenic dyslipidemia who are centrally obese at normal BMI are at similar, and possibly higher, mortality risk compared with those who are centrally obese and overweight or obese according to their BMI. Waist-to-height ratio might be the most pragmatic clinical measure of central obesity. The Mediterranean diet is an effective intervention to prevent ongoing weight gain while reducing abdominal girth. Low levels of exercise can also reduce waist circumference. Weight loss need not be an objective. CONCLUSION: A waist-to-height ratio exceeding 0.5 at normal BMI identifies elevated mortality risk for cardiometabolic disease. This risk might equal or exceed that of centrally obese patients who are overweight or obese. Modest dietary and exercise interventions can be effective in mitigation of this risk.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.028
Threshold uncertainty score0.250

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.011
GPT teacher head0.198
Teacher spread0.187 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations71
Published2019
Admission routes1
Has abstractyes

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