Erratum: A Cross-Sectional Study on Malnutrition in Inflammatory Bowel Disease: Is There a Difference Based on Pediatric or Adult Age Grouping?
Bibliographic record
Abstract
Daniella Serban, MD, previously of McGill University Health Center, Montreal, Quebec, Canada McGill and currently of Iuliu Haţieganu University of Medicine and Pharmacy, Cluj-Napoca, Romania, was inaccurately listed as a co-author of this article with respect to the authorship guidelines and has been removed by the Editors. The correct list of authors is: Valérie Marcil, RD, PhD, Emile Levy, MD, PhD, Devendra Amre, MD, PhD, Alain Bitton, MD, Ana Maria Guilhon de Araújo Sant’Anna, MD, Andrew Szilagy, MD, Daniel Sinnett, PhD, and Ernest G. Seidman, MD. These individuals would like to thank Dr. Serban for her contributions in 2009 to the study concept, data acquisition, and initial data analysis.
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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 | 0.049 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.018 | 0.008 |
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".