Guidelines to Limit Added Sugar Intake
Bibliographic record
Abstract
Letters1 August 2017Guidelines to Limit Added Sugar IntakeMelissa C. Brouwers, PhD, Amir Qaseem, MD, PhD, MHA, Karen Spithoff, MSc, and Ivan D. Florez, MD, MScMelissa C. Brouwers, PhDFrom McMaster University, Hamilton, Ontario, Canada; American College of Physicians, Philadelphia, Pennsylvania; McMaster University, Hamilton, Ontario, Canada; University of Antioquia, Medellin, Colombia.Search for more papers by this author, Amir Qaseem, MD, PhD, MHAFrom McMaster University, Hamilton, Ontario, Canada; American College of Physicians, Philadelphia, Pennsylvania; McMaster University, Hamilton, Ontario, Canada; University of Antioquia, Medellin, Colombia.Search for more papers by this author, Karen Spithoff, MScFrom McMaster University, Hamilton, Ontario, Canada; American College of Physicians, Philadelphia, Pennsylvania; McMaster University, Hamilton, Ontario, Canada; University of Antioquia, Medellin, Colombia.Search for more papers by this author, and Ivan D. Florez, MD, MScFrom McMaster University, Hamilton, Ontario, Canada; American College of Physicians, Philadelphia, Pennsylvania; McMaster University, Hamilton, Ontario, Canada; University of Antioquia, Medellin, Colombia.Search for more papers by this authorAuthor, Article, and Disclosure Informationhttps://doi.org/10.7326/L17-0256 SectionsAboutFull TextPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinkedInRedditEmail TO THE EDITOR:Schillinger and Kearns' editorial (1) states that Erickson and colleagues' use of the Appraisal of Guidelines for Research and Evaluation, 2nd edition (AGREE II), instrument to assess the methodological quality of sugar intake guidelines (2) was inappropriate because “AGREE II is designed for clinical practice guidelines for the treatment of illness.” This statement is incorrect. As the instrument indicates, it “can be applied to guidelines in any disease area targeting any step in the health care continuum, including those for health promotion, public health, screening, diagnosis, treatment or interventions” (3). The 23 items in AGREE II have ...References1. Schillinger D, Kearns C. Guidelines to limit added sugar intake: junk science or junk food? Ann Intern Med. 2017;166:305-6. [PMID: 27992900]. doi:10.7326/M16-2754 LinkGoogle Scholar2. Erickson J, Sadeghirad B, Lytvyn L, Slavin J, Johnston BC. The scientific basis of guideline recommendations on sugar intake. A systematic review. Ann Intern Med. 2017;166:257-67. [PMID: 27992898]. doi:10.7326/M16-2020 LinkGoogle Scholar3. The AGREE Research Trust. Appraisal of Guidelines for Research and Evaluation II Instrument. 2013. Accessed at www.agreetrust.org/wp-content/uploads/2013/10/AGREE-II-Users-Manual-and-23-item-Instrument_2009_UPDATE_2013.pdf on 4 January 2017. Google Scholar4. Brouwers MC, Kho ME, Browman GP, Burgers JS, Cluzeau F, Feder G, et al; AGREE Next Steps Consortium. AGREE II: advancing guideline development, reporting and evaluation in health care. CMAJ. 2010;182:E839-42. [PMID: 20603348] doi:10.1503/cmaj.090449 CrossrefMedlineGoogle Scholar5. Brouwers MC, Kho ME, Browman GP, Burgers JS, Cluzeau F, Feder G, et al; AGREE Next Steps Consortium. Development of the AGREE II, part 1: performance, usefulness and areas for improvement. CMAJ. 2010;182:1045-52. [PMID: 20513780] doi:10.1503/cmaj.091714 CrossrefMedlineGoogle Scholar Author, Article, and Disclosure InformationAffiliations: From McMaster University, Hamilton, Ontario, Canada; American College of Physicians, Philadelphia, Pennsylvania; McMaster University, Hamilton, Ontario, Canada; University of Antioquia, Medellin, Colombia.Disclosures: Disclosures can be viewed at www.acponline.org/authors/icmje/ConflictOfInterestForms.do?msNum=L17-0256. PreviousarticleNextarticle Advertisement FiguresReferencesRelatedDetailsSee AlsoGuidelines to Limit Added Sugar Intake: Junk Science or Junk Food? Dean Schillinger and Cristin Kearns Guidelines to Limit Added Sugar Intake Cristin Kearns and Dean Schillinger Metrics Cited byGuidelines for Medical Nutrition Therapy in Gestational Diabetes Mellitus: Systematic Review and Critical Appraisal 1 August 2017Volume 167, Issue 3Page: 219-220KeywordsDisclosureForecastingHealth careHealth promotionHealth screeningResearch quality assessmentResearch reporting guidelinesTreatment guidelines ePublished: 1 August 2017 Issue Published: 1 August 2017 Copyright & PermissionsCopyright © 2017 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 | 0.005 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.078 | 0.034 |
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".