Suboptimal Monitoring and Dosing of Unfractionated Heparin
Bibliographic record
Abstract
Letters6 April 2004Suboptimal Monitoring and Dosing of Unfractionated HeparinRobert Raschke, MD, MS, Jack Hirsh, MD, and James R. Guidry, PharmDRobert Raschke, MD, MSFrom Good Samaritan Regional Medical Center, Phoenix, AZ 85006; Henderson Civic Hospital Research Center, Hamilton, Ontario L8V 1C3, Canada; and Desert Samaritan Medical Center, Mesa, AZ 85202.Search for more papers by this author, Jack Hirsh, MDFrom Good Samaritan Regional Medical Center, Phoenix, AZ 85006; Henderson Civic Hospital Research Center, Hamilton, Ontario L8V 1C3, Canada; and Desert Samaritan Medical Center, Mesa, AZ 85202.Search for more papers by this author, and James R. Guidry, PharmDFrom Good Samaritan Regional Medical Center, Phoenix, AZ 85006; Henderson Civic Hospital Research Center, Hamilton, Ontario L8V 1C3, Canada; and Desert Samaritan Medical Center, Mesa, AZ 85202.Search for more papers by this authorAuthor, Article, and Disclosure Informationhttps://doi.org/10.7326/0003-4819-140-7-200404060-00031 SectionsAboutFull TextPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinkedInRedditEmail IN RESPONSE:We agree with the comments of Chung and colleagues. We have encountered similar problems and have devised practices for minimizing them. The most important are the development of a close working relationship between interested clinicians and laboratory personnel and proper implementation of standardized heparin order sheets. After years of practice, our coagulation laboratory staff have become adept at calibrating the therapeutic aPTT range for new thromboplastin reagents. We have experienced several major shifts in aPTT therapeutic ranges over the years: Our lowest calibrated range was 46 to 70 seconds, and our highest was 75 to 105 seconds. The ...References1. Raschke RA, Reilly BM, Guidry JR, Fontana JR, Srinivas S. The weight-based heparin dosing nomogram compared with a “standard care” nomogram. A randomized, controlled trial. Ann Intern Med. 1993;119:874-81. [PMID: 8214998] LinkGoogle Scholar2. Raschke RA, Gollihare B, Peirce JC. The effectiveness of implementing the weight-based heparin nomogram as a practice guideline. Arch Intern Med. 1996;156:1645-9. [PMID: 8694662] CrossrefMedlineGoogle Scholar Author, Article, and Disclosure InformationAuthors: Robert Raschke, MD, MS; Jack Hirsh, MD; James R. Guidry, PharmDAffiliations: From Good Samaritan Regional Medical Center, Phoenix, AZ 85006; Henderson Civic Hospital Research Center, Hamilton, Ontario L8V 1C3, Canada; and Desert Samaritan Medical Center, Mesa, AZ 85202. PreviousarticleNextarticle Advertisement FiguresReferencesRelatedDetailsSee AlsoThe Weight-based Heparin Dosing Nomogram Compared with a Standard Care Nomogram Robert A. Raschke , Brendan M. Reilly , James R. Guidry , Joseph R. Fontana , and Sandhya Srinivas Suboptimal Monitoring and Dosing of Unfractionated Heparin in Comparative Studies with Low-Molecular-Weight Heparin Robert Raschke , Jack Hirsh , and James R. Guidry Suboptimal Monitoring and Dosing of Unfractionated Heparin Kevin K. Chung , Jeanne K. Tofferi , and William T. Browne Metrics Cited byHeparin dosing and therapeutic activated partial thromboplastin times (aPTT) in acute coronary syndrome (ACS) 6 April 2004Volume 140, Issue 7Page: 582-583KeywordsClinical trialsHeparinPatientsResearch laboratories ePublished: 6 April 2004 Issue Published: 6 April 2004 Copyright & PermissionsCopyright © 2004 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 | 0.004 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".