Bibliographic record
Abstract
Letters17 December 2013Obesity and Serious InfectionsDimitrios Farmakiotis, MDDimitrios Farmakiotis, MDFrom Baylor College of Medicine, Houston, Texas.Search for more papers by this authorAuthor, Article, and Disclosure Informationhttps://doi.org/10.7326/0003-4819-159-12-201312170-00021 SectionsAboutFull TextPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinkedInRedditEmail TO THE EDITOR:In their recent In the Clinic, Tsai and Wadden (1) include increased susceptibility to influenza, as well as skin and soft tissue infections, in their comprehensive list of obesity-associated health consequences. I would like to highlight some additional issues of potential clinical significance about the well-described but often underrecognized association between obesity and infection (2).In several studies, obesity has been identified as an important risk factor for higher incidence of and worse outcomes from surgical wound, respiratory, periodontal, and urinary tract infections (2–4). Those associations are definitely influenced by comorbid conditions and management pitfalls that occur ...References1. Tsai AG and Wadden TA. In the clinic: obesity. Ann Intern Med. 2013;159:ITC3-1-ITC3-15 LinkGoogle Scholar2. Falagas ME and Kompoti M. Obesity and infection. Lancet Infect Dis. 2006;6:438-46. [PMID: 16790384] CrossrefMedlineGoogle Scholar3. Kwong JC, Campitelli MA, and Rosella LC. Obesity and respiratory hospitalizations during influenza seasons in Ontario, Canada: a cohort study. Clin Infect Dis. 2011;53:413-21. [PMID: 21844024] CrossrefMedlineGoogle Scholar4. Almond MH, Edwards MR, Barclay WS, and Johnston SL. Obesity and susceptibility to severe outcomes following respiratory viral infection. Thorax. 2013;68:684-6. [PMID: 23436045] CrossrefMedlineGoogle Scholar5. Bishara J, Farah R, Mograbi J, Khalaila W, Abu-Elheja O, Mahamid M, et al. Obesity as a risk factor for Clostridium difficile infection. Clin Infect Dis. 2013;57:489-93. [PMID: 23645850] CrossrefMedlineGoogle Scholar Author, Article, and Disclosure InformationAuthors: Dimitrios Farmakiotis, MDAffiliations: From Baylor College of Medicine, Houston, Texas.Disclosures: None disclosed. Forms can be viewed at www.acponline.org/authors/icmje/ConflictOfInterestForms.do?msNum=L13-1075. PreviousarticleNextarticle Advertisement FiguresReferencesRelatedDetailsSee AlsoObesity Adam Gilden Tsai , Thomas A. Wadden Obesity and Serious Infections Adam Gilden Tsai and Thomas A. Wadden Metrics 17 December 2013Volume 159, Issue 12Page: 859KeywordsAntibacterialsAntibioticsBody weightC difficile colitisClostridium difficileCohort studiesCytokinesHospitalizationsImmune systemMedical risk factorsMicrobiomeMorbid obesityObesityOverweightPhagocytosisSoft tissue infectionsT cellsUrinary tract infections ePublished: 17 December 2013 Issue Published: 17 December 2013 Copyright & PermissionsCopyright © 2013 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 | 0.001 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.061 | 0.014 |
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".