Correction: Regulatory Aspects of Optical Methods and Exogenous Targets for Cancer Detection
Bibliographic record
Abstract
In this article (Cancer Res 2017;77;2197–206), which appeared in the May 1, 2017, issue of Cancer Research (1), the authors regret that the author list is incorrect. Because of a miscommunication, T. Joshua Pfefer was not included in the author list. The authors take responsibility for the error. The correct author list and affiliations is as follows:Willemieke S. Tummers1, Jason M. Warram2, Kiranya E. Tipirneni3, John Fengler4, Paula Jacobs5, Lalitha Shankar5, Lori Henderson5, Betsy Ballard6, T. Joshua Pfefer6, Brian W. Pogue7, Jamey P. Weichert8, Michael Bouvet9, Jonathan Sorger10, Christopher H. Contag11, John V. Frangioni12, Michael F. Tweedle13, James P. Basilion14, Sanjiv S. Gambhir15, and Eben L. Rosenthal161Department of Radiology, Molecular Imaging Program, Stanford University, Stanford, California. 2Department of Otolaryngology, University of Alabama at Birmingham, Birmingham, Alabama. 3Department of Surgery, University of Alabama at Birmingham, Birmingham, Alabama. 4NOVADAQ, Burnaby, British Columbia, Canada. 5National Cancer Institute, Bethesda, Maryland. 6U.S. Food and Drug Administration, Silver Spring, Maryland. 7Thayer School of Engineering, Dartmouth College, Hanover, New Hampshire. 8Department of Radiology, University of Wisconsin, Madison, Wisconsin. 9Department of Surgery, University of California San Diego, La Jolla, California. 10Intuitive Surgical Inc., Sunnyvale, California. 11Departments of Pediatrics, Radiology, Microbiology & Immunology, Stanford University, Stanford, California. 12Curadel, LLC, Marlborough, Massachusetts. 13Department of Radiology, Ohio State University, Columbus, Ohio. 14Department of Radiology, Case Western Reserve University, Cleveland, Ohio. 15Departments of Radiology, Bioengineering, and Materials Science & Engineering, Molecular Imaging Program, Stanford University, Stanford, California. 16Department of Otolaryngology, Stanford University, Stanford, California.This article reflects the views of the authors (B. Ballard and T.J. Pfefer) and should not be construed to represent FDA's views or policies.W.S. Tummers and J.M. Warram contributed equally to this article.The online version of the article has been corrected and no longer matches the print.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.092 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.009 | 0.014 |
| Insufficient payload (model declined to judge) | 0.051 | 0.028 |
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".