Oral Pharmacologic Treatment of Type 2 Diabetes Mellitus
Bibliographic record
Abstract
Letters3 July 2012Oral Pharmacologic Treatment of Type 2 Diabetes MellitusHelena W. Rodbard, MD and Paul S. Jellinger, MDHelena W. Rodbard, MDFrom Endocrine and Metabolic Consultants, Rockville, MD 20852; and University of Miami, The Center for Diabetes and Endocrine Care, Hollywood, FL 33021.Search for more papers by this author and Paul S. Jellinger, MDFrom Endocrine and Metabolic Consultants, Rockville, MD 20852; and University of Miami, The Center for Diabetes and Endocrine Care, Hollywood, FL 33021.Search for more papers by this authorAuthor, Article, and Disclosure Informationhttps://doi.org/10.7326/0003-4819-157-1-201207030-00017 SectionsAboutFull TextPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinkedInRedditEmail TO THE EDITOR:The recently published American College of Physicians (ACP) clinical guidelines for management of patients with diabetes (1) fail to consider algorithms developed by the American Association of Clinical Endocrinologists/American College of Endocrinology (AACE/ACE) (2) (for which we served as co-chairs of the task force that developed this algorithm), American Diabetes Association (ADA) (3), Canadian Diabetes Association (4), and Yale Diabetes Center (5). Although these algorithms were developed by specialists, they were specifically intended for use by internists and primary care physicians who treat most patients with type 2 diabetes. The AACE/ACE and ADA algorithms recommend target levels ...References1. Qaseem A, Humphrey LL, Sweet DE, Starkey M, Shekelle P; Clinical Guidelines Committee of the American College of Physicians. Oral pharmacologic treatment of type 2 diabetes mellitus: a clinical practice guideline from the American College of Physicians. Ann Intern Med. 2012;156:218-31. [PMID: 22312141] LinkGoogle Scholar2. Rodbard HW, Jellinger PS, Davidson JA, Einhorn D, Garber AJ, Grunberger G, et al. Statement by an American Association of Clinical Endocrinologists/American College of Endocrinology consensus panel on type 2 diabetes mellitus: an algorithm for glycemic control. Endocr Pract. 2009;15:540-59. [PMID: 19858063] CrossrefMedlineGoogle Scholar3. Nathan DM, Buse JB, Davidson MB, Ferrannini E, Holman RR, Sherwin R, et al; American Diabetes Association. Medical management of hyperglycemia in type 2 diabetes: a consensus algorithm for the initiation and adjustment of therapy: a consensus statement of the American Diabetes Association and the European Association for the Study of Diabetes. Diabetes Care. 2009;32:193-203. [PMID: 18945920] CrossrefMedlineGoogle Scholar4. Woo V. Important differences: Canadian Diabetes Association 2008 clinical practice guidelines and the consensus statement of the American Diabetes Association and the European Association for the Study of Diabetes [Letter]. Diabetologia. 2009;52:552-3. [PMID: 19107458] CrossrefMedlineGoogle Scholar5. Inzucchi SE. Diabetes Facts and Guidelines 2011–2012: Type 2 DM Treatment Algorithms. Accessed at endocrinology.yale.edu/patient/50135_Yale%20National%20F.pdf 15 February 2012. Google Scholar Author, Article, and Disclosure InformationAuthors: Helena W. Rodbard, MD; Paul S. Jellinger, MDAffiliations: From Endocrine and Metabolic Consultants, Rockville, MD 20852; and University of Miami, The Center for Diabetes and Endocrine Care, Hollywood, FL 33021.Disclosures: Dr. Rodbard: Consultancy: Novo Nordisk, Amylin, Novartis, Merck; Payment for lectures including service on speakers bureaus: Amylin, Bristol-Myers Squibb, Boehringer Ingelheim, Lilly, Merck, Novo Nordisk, Sanofi. Dr. Jellinger: Consultancy: Amylin, Merck, Novo Nordisk; Payment for lectures including service on speakers bureaus: Amylin, Merck, Novo Nordisk, Boehringer-Ingleheim. PreviousarticleNextarticle Advertisement FiguresReferencesRelatedDetailsSee AlsoOral Pharmacologic Treatment of Type 2 Diabetes Mellitus: A Clinical Practice Guideline From the American College of Physicians Amir Qaseem , Linda L. Humphrey , Donna E. Sweet , Melissa Starkey , and Paul Shekelle , for the Clinical Guidelines Committee of the American College of Physicians* Metrics 3 July 2012Volume 157, Issue 1Page: 75-76KeywordsAlgorithmsEndocrinologyHbA1cHypoglycemiaPrimary care physiciansProtease inhibitorsSafetyType 2 diabetesWeight gainWeight loss ePublished: 3 July 2012 Issue Published: 3 July 2012 Copyright & PermissionsCopyright © 2012 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.034 | 0.008 |
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".