Correction: The Effects of Folate Supplementation on Diabetes Biomarkers Among Patients with Metabolic Diseases: A Systematic Review and Meta-Analysis of Randomized Controlled Trials
Bibliographic record
Abstract
Correction to: The Effects of Folate Supplementation on Diabetes Biomarkers Among Patients with Metabolic Diseases: A Systematic Review and Meta-Analysis of Randomized Controlled Trials Horm Metab Res 2018; 50(02): 93-105 DOI: 10.1055/s-0043-125148 Corrigendum The Effects of Folate Supplementation on Diabetes Biomarkers Among Patients with Metabolic Diseases: A Systematic Review and Meta-Analysis of Randomized Controlled Trials Maryam Akbari, Reza Tabrizi, Kamran B. Lankarani, Seyed Taghi Heydari, Maryam Karamali, Fariba Keneshlou, Kayvan Niknam, Fariba Kolahdooz, Zatollah Asemi Horm Metab Res 2018 Published online 17.01.2018 DOI: 10.1055/s-0043-125148 Withdrawal of Co-Authorship by Maryam Kashanian Maryam Kashanian has requested for her authorship to be withdrawn from this paper as she did neither know of nor approve of her co-authorship. Publication History Article published online: 02 October 2020 © 2020. Thieme. All rights reserved. © Georg Thieme Verlag KG Stuttgart · New York
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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 | 0.035 | 0.310 |
| Meta-epidemiology (narrow) | 0.005 | 0.004 |
| Meta-epidemiology (broad) | 0.011 | 0.008 |
| Bibliometrics | 0.008 | 0.015 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.008 | 0.003 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.109 | 0.023 |
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".