Christine Laine: Internist, Medical Journalist, and Editor, <i>Annals of Internal Medicine</i>—2009
Bibliographic record
Abstract
Editorials6 October 2009Christine Laine: Internist, Medical Journalist, and Editor, Annals of Internal Medicine—2009Eric B. Larson, MD, MPHEric B. Larson, MD, MPHFrom Group Health Research Institute, Seattle, WA 98101-1448.Search for more papers by this authorAuthor, Article, and Disclosure Informationhttps://doi.org/10.7326/0003-4819-151-7-200910060-00013 SectionsAboutFull TextPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinkedInRedditEmail The Annals of Internal Medicine has chronicled research and scholarship of internal medicine for 83 years, ably serving many generations of internists and nourishing the intellectual curiosity and passion for learning that uniquely characterizes internal medicine specialists. The journal also serves an important archival function, providing a place where researchers can deposit the results of their scientific endeavors—a function that legendary Annals editor Ed Huth established and defended as Annals chronicled numerous papers related to emergence of new and important conditions (such as AIDS and Legionnaire disease) even before the research had any immediate value for clinicians caring for individual ...References1. Sox HC. Medical journal editing: who shall pay? [Editorial]. Ann Intern Med. 2009;151:68-9. [PMID: 19581649] LinkGoogle Scholar2. Laine C. Christine Laine: embracing the challenges of medical journalism. Interview by Kelly Morris. Lancet. 2009;373:1839. [PMID: 19482203] CrossrefMedlineGoogle Scholar3. Moynihan R, Cassels A. Selling Sickness. Vancouver: Greystone Books; 2005. Google Scholar Author, Article, and Disclosure InformationAuthors: Eric B. Larson, MD, MPHAffiliations: From Group Health Research Institute, Seattle, WA 98101-1448.Disclosures: None disclosed.Corresponding Author: Eric B. Larson, MD, MPH, Executive Director, Group Health Research Institute, 1730 Minor Avenue, Suite 1600, Seattle, WA 98101-1448. PreviousarticleNextarticle Advertisement FiguresReferencesRelatedDetails Metrics 6 October 2009Volume 151, Issue 7Page: 511-512KeywordsDietForecastingHealth careHealth information technologyIntelligenceInternetLegionellosisPublic policyRiversScientists ePublished: 6 October 2009 Issue Published: 6 October 2009 Copyright & PermissionsCopyright © 2009 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 | 0.003 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.035 | 0.029 |
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".