Savings from Canadian Internet Pharmacies Are Limited
Bibliographic record
Abstract
Letters7 February 2006Savings from Canadian Internet Pharmacies Are LimitedMark J. Eisenberg, MD, MPH, Rafael Firszt, MD, MBA, and Bradley Quon, MD, MBAMark J. Eisenberg, MD, MPHFrom Jewish General Hospital, McGill University, Montréal, Québec, H3T 1E2 Canada.Search for more papers by this author, Rafael Firszt, MD, MBAFrom Jewish General Hospital, McGill University, Montréal, Québec, H3T 1E2 Canada.Search for more papers by this author, and Bradley Quon, MD, MBAFrom Jewish General Hospital, McGill University, Montréal, Québec, H3T 1E2 Canada.Search for more papers by this authorAuthor, Article, and Disclosure Informationhttps://doi.org/10.7326/0003-4819-144-3-200602070-00027 SectionsAboutFull TextPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinkedInRedditEmail IN RESPONSE:We agree with Dr. Rothberg that Americans should not have to look north of the border to access more affordable medications. The underlying premise of our article was not to promote the purchase of Canadian pharmaceuticals but to quantify and highlight the differences in brand-name medication prices between the 2 countries. The average savings of 24% per unit that we cited should serve as a benchmark for Congress to target in lowering brand-name medication prices domestically for all Americans. Canadian-style strategies to lower medication prices, including price control through the Patented Medicines Price Review Board and the use ...Reference1. Pharmacy Benefits Management Strategic Healthcare Group. Drug and pharmaceutical prices. Accessed at www.pbm.va.gov/PBM/prices.htm on 17 November 2005. Google Scholar Author, Article, and Disclosure InformationAffiliations: From Jewish General Hospital, McGill University, Montréal, Québec, H3T 1E2 Canada.Disclosures: None disclosed. PreviousarticleNextarticle Advertisement FiguresReferencesRelatedDetailsSee AlsoA Comparison of Brand-Name Drug Prices between Canadian-Based Internet Pharmacies and Major U.S. Drug Chain Pharmacies Bradley S. Quon , Rafael Firszt , and Mark J. Eisenberg Savings from Canadian Internet Pharmacies Are Limited Michael Rothberg Metrics 7 February 2006Volume 144, Issue 3Page: 224-225KeywordsConflicts of interestDrug pricesDrug regulationDrugsInternet ePublished: 7 February 2006 Issue Published: 7 February 2006 Copyright & PermissionsCopyright © 2006 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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.653 | 0.424 |
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".