Corrigendum: Case report: Possible role of low-dose PEM for avoiding unneeded procedures associated with false-positive or equivocal breast MRI results
Bibliographic record
Abstract
Error in Author ListIn the published article, there was an error in the author list order. When author Anna Marie Mulligan was added, she was erroneously listed as the final author when that position should have been for the primary investigator, Alla Reznik. The correct author list and affiliations should be:Madeline Rapley1 *, Vivianne Freitas 2 , Irving N. Weinberg3 , Brandon Baldassi 4 , Harutyun Poladyan1 , Michael Waterston 4 , Sandeep Ghai 2 , Samira Taeb5 , Oleksandr Bubon1,4, Anna Marie Mulligan6 and Alla Reznik1,71 Department of Physics, Lakehead University, Thunder Bay, ON, Canada, 2Temerty Faculty of Medicine, Joint Department of Medical Imaging, University of Toronto, Toronto, ON, Canada, 3Weinberg Medical Physics, Rockville, MD, United States, 4Radialis Inc., Thunder Bay, ON, Canada, 5Department of Research, Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada, 6 Laboratory Medicine Program, University Health Network – Toronto General Hospital Site, University of Toronto, Toronto, ON, Canada, 7Thunder Bay Regional Health Sciences Centre, Thunder Bay, ON, CanadaThe authors apologize for this error and state that this does not change the scientific conclusions of the article in any way. The original article has been updated.
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 | 0.001 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.011 | 0.007 |
| Insufficient payload (model declined to judge) | 0.018 | 0.010 |
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".